top of page
Publications
Metastable sodium closo-hydridoborates for all-solid-state batteries with thick cathodes
Jin An Sam Oh, Zihan Yu, Chen-Jui Huang, Phillip Ridley, Alex Liu, Tianren Zhang, Bing Joe Hwang, Kent J. Griffith, Shyue Ping Ong, Ying Shirley Meng
Joule, 2025, 102130
Abstract
All-solid-state batteries (ASSBs) featuring a thick cathode layer paired with a high-capacity alloy anode offer enhanced energy density and reliable performance, even at subzero temperatures, and can outperform their liquid-based counterparts. Enabling such technology requires a solid electrolyte with high ionic conductivity, mechanical formability, and excellent electrochemical stability. Here, we demonstrate that a kinetically stable orthorhombic Na3(B12H12)(BH4) phase exhibits a superionic conductivity of 4.6 mS cm−1 at 30°C alongside excellent reduction stability. High-throughput molecular dynamic simulations reveal that the propensity for anion motion significantly enhances the population of highly mobile Na+ without affecting the activation energy. By leveraging its high conductivity across a wide temperature range, this material enables the development of all-solid-state sodium-ion batteries with ultra-thick cathodes, delivering reliable functionality at room temperature and in subzero environments. This study expands our understanding of hydridoborate-based solid electrolytes, highlighting their potential in next-generation energy storage systems.
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille Bilodeau, Keith Brown, Emmanuel Candes, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor Coley, Andrew Connolly, Fred Daum, Sijia Dong, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos, Nicolás García Trillos, Cecilia Garraffo, Robert Ghrist, Rafael Gomez-Bombarelli, Gianluca Guadagni, Sreelekha Guggilam, Sergei Gukov, Juan B. Gutiérrez, Salman Habib, Johannes Hachmann, Boris Hanin, Philip Harris, Murray Holland, Elizabeth Holm, Hsin-Yuan Huang, Shih-Chieh Hsu, Nick Jackson, Olexandr Isayev, Heng Ji, Aggelos Katsaggelos, Jeremy Kepner, Yannis Kevrekidis, Michelle Kuchera, J. Nathan Kutz, Branislava Lalic, Ann Lee, Matt LeBlanc, Josiah Lim, Rebecca Lindsey, Yongmin Liu, Peter Y. Lu, Sudhir Malik, Vuk Mandic, Vidya Manian, Emeka P. Mazi, Pankaj Mehta, Peter Melchior, Brice Ménard, Jennifer Ngadiuba, Stella Offner, Elsa Olivetti, Shyue Ping Ong, Christopher Rackauckas, Philippe Rigollet, Chad Risko, Philip Romero, Grant Rotskoff, Brett Savoie, Uros Seljak, David Shih, Gary Shiu, Dima Shlyakhtenko, Eva Silverstein, Taylor Sparks, Thomas Strohmer, Christopher Stubbs, Stephen Thomas, Suriyanarayanan Vaikuntanathan, Rene Vidal, Francisco Villaescusa-Navarro, Gregory Voth, Benjamin Wandelt, Rachel Ward, Melanie Weber, Risa Wechsler, Stephen Whitelam, Olaf Wiest, Mike Williams, Zhuoran Yang, Yaroslava G. Yingling, Bin Yu, Shuwen Yue, Ann Zabludoff, Huimin Zhao, Tong Zhang
arXiv, 2025
Abstract
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.
Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Tsz Wai Ko, Bowen Deng, Marcel Nassar, Luis Barroso-Luque, Runze Liu, Ji Qi, Atul C. Thakur, Adesh Rohan Mishra, Elliott Liu, Gerbrand Ceder, Santiago Miret, Shyue Ping Ong
npj Computational Materials, 2025, 11, 1, 253
Abstract
Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.
Accelerated data-driven materials science with the Materials Project
Matthew K. Horton, Patrick Huck, Ruo Xi Yang, Jason M. Munro, Shyam Dwaraknath, Alex M. Ganose, Ryan S. Kingsbury, Mingjian Wen, Jimmy X. Shen, Tyler S. Mathis, Aaron D. Kaplan, Karlo Berket, Janosh Riebesell, Janine George, Andrew S. Rosen, Evan W. C. Spotte-Smith, Matthew J. McDermott, Orion A. Cohen, Alex Dunn, Matthew C. Kuner, Gian-Marco Rignanese, Guido Petretto, David Waroquiers, Sinead M. Griffin, Jeffrey B. Neaton, Daryl C. Chrzan, Mark Asta, Geoffroy Hautier, Shreyas Cholia, Gerbrand Ceder, Shyue Ping Ong, Anubhav Jain, Kristin A. Persson
Nature Materials, 2025
Abstract
The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.
Data-efficient construction of high-fidelity graph deep learning interatomic potentials
Tsz Wai Ko, Shyue Ping Ong
npj Computational Materials, 2025, 11, 1, 65
Abstract
Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations. However, most MLPs today are trained on data computed using relatively cheap density functional theory (DFT) methods such as the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) functional. While meta-GGAs such as the strongly constrained and appropriately normed (SCAN) functional have been shown to yield significantly improved descriptions of atomic interactions for diversely bonded systems, their higher computational cost remains an impediment to their use in MLP development. In this work, we outline a data-efficient multi-fidelity approach to constructing Materials 3-body Graph Network (M3GNet) interatomic potentials that integrate different levels of theory within a single model. Using silicon and water as examples, we show that a multi-fidelity M3GNet model trained on a combined dataset of low-fidelity GGA calculations with 10% of high-fidelity SCAN calculations can achieve accuracies comparable to a single-fidelity M3GNet model trained on a dataset comprising 8 × the number of SCAN calculations. This work provides a pathway to the development of high-fidelity MLPs in a cost-effective manner by leveraging existing low-fidelity datasets.
A Foundational Potential Energy Surface Dataset for Materials
Aaron D. Kaplan, Runze Liu, Ji Qi, Tsz Wai Ko, Bowen Deng, Janosh Riebesell, Gerbrand Ceder, Kristin A. Persson, Shyue Ping Ong
arXiv, 2025
Abstract
Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs)$^{1-3}$ offer a computationally efficient alternative to density functional theory (DFT)$^4$ for PES modeling across the periodic table. However, their accuracy today is fundamentally constrained due to a reliance on DFT relaxation data.$^{5,6}$ Here, we introduce MatPES, a foundational PES dataset comprising $\sim 400,000$ structures carefully sampled from 281 million molecular dynamics snapshots that span 16 billion atomic environments. We demonstrate that UMLIPs trained on the modestly sized MatPES dataset can rival, or even outperform, prior models trained on much larger datasets across a broad range of equilibrium, near-equilibrium, and molecular dynamics property benchmarks. We also introduce the first high-fidelity PES dataset based on the revised regularized strongly constrained and appropriately normed (r$^2$SCAN) functional$^7$ with greatly improved descriptions of interatomic bonding. The open source MatPES initiative emphasizes the importance of data quality over quantity in materials science and enables broad community-driven advancements toward more reliable, generalizable, and efficient UMLIPs for large-scale materials discovery and design.
Increasing the Energy Density of Disordered Rock Salt Anodes for Fast-Charging Lithium-Ion Batteries
Haichen Lin, Wei-Tao Peng, Zishen Wang, Jan Hofmann, Simon M. Vornholt, Haodong Liu, Shen Wang, John Holoubek, Ke Zhou, Qiushi Miao, Steven Huber, Karena W. Chapman, Shyue Ping Ong, Ping Liu
ACS Materials Letters, 2025, 699-706
Abstract
Transition-metal oxides (TMOs) are promising anode materials for safe and fast-charging batteries, but their high operating potentials limit energy density. Here, we develop a strategy to suppress the operating potential of the disordered rock salt (DRS) Li3V2O5 (LVO) anode by ∼10% to 0.54 V via Mg doping. Density functional theory (DFT) calculations attribute this voltage reduction to increased site energy of Li ions because of Mg doping, with minimal impact on Li migration barriers. Mgdoped LVO retains over 95% of its capacity over 1000 cycles at a rate of 5 C. Full cells with a LiNi0.8Co0.1Mn0.1O2 cathode demonstrate the expected increase in cell voltage and energy density while retaining 91% of their capacity over 250 cycles at 5 C. Our findings show that Mg doping provides a promising pathway for designing fast-charging, long-cycle-life anode materials with enhanced energy density.
Superionic Surface Li-Ion Transport in Carbonaceous Materials
Jianbin Zhou, Shen Wang, Chaoshan Wu, Ji Qi, Hongli Wan, Shen Lai, Tsz Wai Ko, Zhaohui Liang, Shijie Feng, Ke Zhou, Nimrod Harpak, Mengchen Liu, Zeyu Hui, Paulina J Ai, Haodong Liu, Wenlin Yan, Yang Ha, Min-Jae Kim, Kent Griffith, Chunsheng Wang, Shyue Ping Ong, Yan Yao, Ping Liu
Nano Letters, 2025
Abstract
Unlike Li-ion transport in the bulk of carbonaceous materials, little is known about Li-ion diffusion on their surface. In this study, we have discovered an ultrafast Li-ion transport phenomenon on the surface of carbonaceous materials with limited reversible Li insertion capacity and high surface area. An ionic conductivity of 18.1 mS cm−1 at room temperature is observed in lithiated Ketjen black (KB), far exceeding those of most solid-state ion conductors. Theoretical calculations reveal low diffusion barriers for the surface Li species. As a result, lithiated KB functions effectively as an interlayer between Li and solid-state electrolytes (SSEs) to mitigate dendrite growth. Further, lithiated KB acts as a high-performance mixed ionic−electronic conductor and replaces solid electrolytes to enhance graphite anode performance, demonstrating full utilization with ∼85% capacity retention over 300 cycles. The discovery of this surface-mediated ultrafast Li-ion transport mechanism provides new directions for the design of solid-state ion conductors and solid-state batteries.
Effect of Processing Conditions on Short-Range Order and Mechanical Properties of the NbMoTaW Multi-Principal Element Alloy
Hui Zheng, Luke Nibbelink, Xiang-Guo Li, Yunxing Zuo, Chi Chen, Shyue Ping Ong
High Entropy Alloys & Materials, 2025
Abstract
Refractory multi-principal element alloys (RMPEAs) are promising candidates for high-temperature applications due to their exceptional mechanical properties. In this work, we investigate the effect of processing conditions on the observed short-range order (SRO) in the NbMoTaW RMPEA, and the consequent effect on mechanical properties, using Monte Carlo/ molecular dynamics (MC/MD) simulations with a highly accurate machine learning interatomic potential. We demonstrate that SRO is maximized at an intermediate temperature range of 800K–1000K. A higher SRO results in higher stacking fault energies (SFEs), anti-phase boundary energies (APBEs), and critical resolved shear stresses (CRSS). We also show that Nb segregation in polycrystalline NbTaMoW is also present at intermediate annealing temperatures, and this Nb segregation can have the effect of retarding grain growth even at high temperatures.
A practical guide to machine learning interatomic potentials – Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gabor Csanyi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, Kj Schmidt, So Takamoto, Aidan P. Thompson, Julia Westermayr, Brandon M. Wood
Current Opinion in Solid State and Materials Science, 2025, 35, 101214
Abstract
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3–10+ years.
Proton-exchange induced reactivity in layered oxides for lithium-ion batteries
Panpan Xu, Xingyu Guo, Binglei Jiao, Jinxing Chen, Minghao Zhang, Haodong Liu, Xiaolu Yu, Maura Appleberry, Zhenzhen Yang, Hongpeng Gao, Fan Yang, Xuefei Weng, Yanbin Shen, Jing Gu, Ying Shirley Meng, Christopher Brooks, Shyue Ping Ong, Zheng Chen
Nature Communications, 2024, 15, 1, 9842
Abstract
LiNixCoyMn1-x-yO2 (0 < x, y
Enhanced Cycling Stability of All-Solid-State Lithium–Sulfur Battery through Nonconductive Polar Hosts
Tianwei Jin, Keyue Liang, Jeong-Hoon Yu, Ting Wang, Yihan Li, Tai-De Li, Shyue Ping Ong, Jong-Sung Yu, Yuan Yang
Nano Letters, 2024, 24, 22, 6625–6633
Abstract
All-solid-state lithium−sulfur batteries (ASSLSBs) are promising next-generation battery technologies with a high energy density and excellent safety. Because of the insulating nature of sulfur/Li2S, conventional cathode designs focus on developing porous hosts with high electronic conductivities such as porous carbon. However, carbon hosts boost the decomposition of sulfide electrolytes and suffer from sulfur detachment due to their weak bonding with sulfur/Li2S, resulting in capacity decays. Herein, we propose a counterintuitive design concept of host materials in which nonconductive polar mesoporous hosts can enhance the cycling life of ASSLSBs through mitigating the decomposition of adjacent electrolytes and bonding sulfur/Li2S steadily to avoid detachment. By using a mesoporous SiO2 host filled with 70 wt % sulfur as the cathode, we demonstrate steady cycling in ASSLSBs with a capacity reversibility of 95.1% in the initial cycle and a discharge capacity of 1446 mAh/g after 500 cycles at C/5 based on the mass of sulfur.
Healable and conductive sulfur iodide for solid-state Li–S batteries
Jianbin Zhou, Manas Likhit Holekevi Chandrappa, Sha Tan, Shen Wang, Chaoshan Wu, Howie Nguyen, Canhui Wang, Haodong Liu, Sicen Yu, Quin R. S. Miller, Gayea Hyun, John Holoubek, Junghwa Hong, Yuxuan Xiao, Charles Soulen, Zheng Fan, Eric E. Fullerton, Christopher J. Brooks, Chao Wang, Raphaële J. Clément, Yan Yao, Enyuan Hu, Shyue Ping Ong, Ping Liu
Nature, 2024, 1-5
Abstract
Solid-state Li–S batteries (SSLSBs) are made of low-cost and abundant materials free of supply chain concerns. Owing to their high theoretical energy densities, they are highly desirable for electric vehicles1–3. However, the development of SSLSBs has been historically plagued by the insulating nature of sulfur4,5 and the poor interfacial contacts induced by its large volume change during cycling6,7, impeding charge transfer among different solid components. Here we report an S9.3I molecular crystal with I2 inserted in the crystalline sulfur structure, which shows a semiconductor-level electrical conductivity (approximately 5.9 × 10−7 S cm−1) at 25 °C; an 11-order-of-magnitude increase over sulfur itself. Iodine introduces new states into the band gap of sulfur and promotes the formation of reactive polysulfides during electrochemical cycling. Further, the material features a low melting point of around 65 °C, which enables repairing of damaged interfaces due to cycling by periodical remelting of the cathode material. As a result, an Li–S9.3I battery demonstrates 400 stable cycles with a specific capacity retention of 87%. The design of this conductive, low-melting-point sulfur iodide material represents a substantial advancement in the chemistry of sulfur materials, and opens the door to the practical realization of SSLSBs.
Roadmap for the development of machine learning-based interatomic potentials
Yong-Wei Zhang, Viacheslav Sorkin, Zachary H Aitken, Antonio Politano, Joerg Behler, Aidan Thompson, Tsz Wai Ko, Shyue Ping Ong, Olga Chalykh, Dmitry Korogod, Evgeny Podryabinkin, Alexander V. Shapeev, Ju Li, Yuri Mishin, Zongrui Pei, Xianglin Liu, Jaesun Kim, Yutack Park, Seungwoo Hwang, Seungwu Han, Killian Sheriff, Yifan Cao, Rodrigo Freitas
Modelling and Simulation in Materials Science and Engineering, 2024
Abstract
An interatomic potential, traditionally regarded as a mathematical function, serves to depict atomic interactions within molecules or solids by expressing potential energy concerning atom positions. These potentials are pivotal in materials science and engineering, facilitating atomic-scale simulations, predictive material behaviour, accelerated discovery, and property optimization. Notably, the landscape is evolving with machine learning transcending conventional mathematical models. Various machine learning-based interatomic potentials, such as artificial neural networks (CNN), kernel-based methods, deep learning, and physics-informed models, have emerged, each wielding unique strengths and limitations. These methods decode the intricate connection between atomic configurations and potential energies, offering advantages like precision, adaptability, insights, and seamless integration. The transformative potential of machine learning-based interatomic potentials looms large in materials science and engineering. They promise tailor-made materials discovery and optimized properties for specific applications. Yet, formidable challenges persist, encompassing data quality, computational demands, transferability, interpretability, and robustness. Tackling these hurdles is imperative for nurturing accurate, efficient, and dependable machine learning-based interatomic potentials primed for widespread adoption in materials science and engineering. This roadmap offers an appraisal of the current machine learning-based interatomic potential landscape, delineates the associated challenges, and envisages how progress in this domain can empower atomic-scale modeling of the composition-processing-microstructure-property relationship, underscoring its significance in materials science and engineering.
Barium Vacancies as the Origin of Triboluminescence in Hexacelsian Ceramics: An Ab Initio and Experimental Investigation
Ekaterina Novitskaya, Mahdi Amachraa, Fabián Martínez-Pallares, Frank Güell, Virginia Gómez-Vidales, Shyue Ping Ong, Manuel Herrera, Olivia A Graeve
Advanced Optical Materials, 2024
Abstract
We describe the triboluminescence response of undoped (BaAl2Si2O8, h−BAS) and Eu-doped (h−BAS:Eu) barium hexacelsian powders and show that the triboluminescence behavior is dependent on the formation of barium vacancies. X-ray photoelectron spectroscopy of the h−BAS:Eu powders confirms the presence of Eu3+ and Eu2+ in the compound, leading to the formation of significant vacancy point defects in excess of those found in h−BAS as a result of the charge imbalance caused by the substitution of Eu3+ in Ba2+ sites. From electron paramagnetic resonance measurements and density functional theory (DFT) calculations, we demonstrate that the vacancy defects correspond to singly ionized barium vacancies. DFT-calculated thermodynamic transitions and electronic structure calculations reveal deep energy levels within the compound’s energy band gap, with a strong emission at 3.33 eV correlated to an electron exchange between the conduction band minimum and a barium vacancy center. Time-resolved triboluminescence spectra show that the increased concentration of barium vacancies in h−BAS:Eu enhances the signal by about 75% compared to the signal from h−BAS. These results play an important role in the understanding of fundamental mechanisms behind the triboluminescence response of ceramic materials as well as the role of different types of defects in this process.
Influence of Interlayer Cation Ordering on Na Transport in P2-Type Na0.67–xLiy Ni0.33–zMn0.67+zO2 for Sodium-Ion Batteries
Eric Gabriel, Zishen Wang, Vibhu Vardhan Singh, Kincaid Graff, Jue Liu, Cyrus Koroni, Dewen Hou, Darin Schwartz, Cheng Li, Juejing Liu, Xiaofeng Guo, Naresh C Osti, Shyue Ping Ong, Hui Xiong
Journal of the American Chemical Society, 2024
Abstract
P2-type Na2/3Ni1/3Mn2/3O2 (PNNMO) has been extensively studied because of its desirable electrochemical properties as a positive electrode for sodium-ion batteries. PNNMO exhibits intralayer transition-metal ordering of Ni and Mn and intralayer Na+/vacancy ordering. The Na+/vacancy ordering is often considered a major impediment to fast Na+ transport and can be affected by transitionmetal ordering. We show by neutron/X-ray diffraction and density functional theory (DFT) calculations that Li doping (Na2/3Li0.05Ni1/3Mn2/3O2, LFN5) promotes ABC-type interplanar Ni/ Mn ordering without disrupting the Na+/vacancy ordering and creates low-energy Li−Mn-coordinated diffusion pathways. A structure model is developed to quantitatively identify both the intralayer cation mixing and interlayer cationic stacking fault densities. Quasielastic neutron scattering reveals that the Na+ diffusivity in LFN5 is enhanced by an order of magnitude over PNNMO, increasing its capacity at a high current. Na2/3Ni1/4Mn3/4O2 (NM13) lacks Na+/vacancy ordering but has diffusivity comparable to that of LFN5. However, NM13 has the smallest capacity at a high current. The high site energy of Mn−Mn-coordinated Na compared to that of Ni−Mn and higher density of Mn−Mn-coordinated Na+ sites in NM13 disrupts the connectivity of low-energy Ni−Mn-coordinated diffusion pathways. These results suggest that the interlayer ordering can be tuned through the control of composition, which has an equal or greater impact on Na+ diffusion than the Na+/vacancy ordering.
Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling
Ji Qi, Tsz Wai Ko, Brandon C Wood, Tuan Anh Pham, Shyue Ping Ong
npj Computational Materials, 2024, 10, 43, 1-11
Abstract
Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods and play an increasingly important role in the study and design of materials. However, MLIPs are only as accurate and robust as the data on which they are trained. Here, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolates more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with the M3GNet universal potential can be used instead of expensive ab initio MD to rapidly create a large configuration space for target systems. We combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures. This work paves the way for robust high-throughput development of MLIPs across any compositional complexity.
Investigating the composition-microstructure-property relationship in two dimensions in a new class of compositionally complex solid electrolytes
Shu-Ting Ko, Tom Lee, Jose Arturo Venegas, Shyue Ping Ong, Xiaoqing Pan, Jian Luo
Journal of the European Ceramic Society, 2024, 117126
Abstract
This study investigates the synergistic composition-microstructure-property relationships in a new class of compositionally complex perovskite oxides (CCPOs) as Li-conductive solid electrolytes. A matrix of compounds with formula (Li0.375Sr0.4375)(Ta0.75(1-y)Nb0.75yZr0.25(1-z)Hf0.25z)O3-δ (y, z = 0, 0.5, or 1) are synthesized and characterized. Correlations among composition, structural distortion, microstructure, interfaces, and ionic conductivity are systematically investigated. It is found that Nb5+ substitution in B sites promotes densification and grain growth, while Hf4+ addition expands crystal lattice, which boost interface and bulk ionic transport, respectively. Notably, (Li0.375Sr0.4375)(Ta0.375Nb0.375Hf0.25)O3-δ achieves an improved ionic conductivity of ~0.336 mS/cm, occurring concurrently with a large BO6 distortion that enhances bulk ionic conduction and a large grain size that reduces the total grain boundary resistivity. This work represents the first in-depth experimental investigation of the composition-microstructure-property relationship of compositionally complex ceramics (CCCs) in two compositional dimensions, and it exemplifies a pathway to tailor properties in multiple compositional dimensions.
Machine learning moment tensor potential for modeling dislocation and fracture in L1_0-TiAl and D0_19-Ti3Al alloys
Ji Qi, Z. H. Aitken, Qingxiang Pei, Anne Marie Z. Tan, Yunxing Zuo, M. H. Jhon, S. S. Quek, T. Wen, Zhaoxuan Wu, Shyue Ping Ong
Physical Review Materials, 2023, 7, 10, 103602
Abstract
Dual-phase γ-TiAl and α2−Ti3Al alloys exhibit high strength and creep resistance at high temperatures. However, they suffer from low tensile ductility and fracture toughness at room temperature. Experimental studies show unusual plastic behavior associated with ordinary and superdislocations, making it necessary to gain a detailed understanding on their core properties in individual phases and at the two-phase interfaces. Unfortunately, extended superdislocation cores are widely dissociated beyond the length scales practical for routine first-principles density-functional theory (DFT) calculations, while extant interatomic potentials are not quantitatively accurate to reveal mechanistic origins of the unusual core-related behavior in either phases. Here, we develop a highly accurate moment tensor potential (MTP) for the binary Ti-Al alloy system using a DFT dataset covering a broad range of intermetallic and solid solution structures. The optimized MTP is rigorously benchmarked against both previous and new DFT calculations, and unlike existing potentials, is shown to possess outstanding accuracy in nearly all tested mechanical properties, including lattice parameters, elastic constants, surface energies, and generalized stacking fault energies (GSFE) in both phases. The utility of the MTP is further demonstrated by producing dislocation core structures largely consistent with expectations from DFT-GSFE and experimental observations. The new MTP opens the path to realistic modeling and simulations of bulk lattice and defect properties relevant to the plastic deformation and fracture processes in γ-TiAl and α2−Ti3Al dual-phase alloys.
Compositionally complex perovskite oxides: Discovering a new class of solid electrolytes with interface-enabled conductivity improvements
Shu-Ting Ko, Tom Lee, Ji Qi, Dawei Zhang, Wei-Tao Peng, Xin Wang, Wei-Che Tsai, Shikai Sun, Zhaokun Wang, William J. Bowman, Shyue Ping Ong, Xiaoqing Pan, Jian Luo
Matter, 2023, 0, 0
Abstract
Compositionally complex ceramics (CCCs), including high-entropy ceramics, offer a vast, unexplored compositional space for materials discovery. Herein, we propose and demonstrate strategies for tailoring CCCs via a combination of non-equimolar compositional designs and control of grain boundaries (GBs) and microstructures. Using oxide solid electrolytes for all-solid-state batteries as an example, we have discovered a class of compositionally complex perovskite oxides (CCPOs) with improved lithium ionic conductivities beyond the limit of conventional doping. For example, we demonstrate that the ionic conductivity can be improved by >60% in (Li0.375Sr0.4375)(Ta0.375Nb0.375Zr0.125Hf0.125)O3-δ compared with the (Li0.375Sr0.4375)(Ta0.75Zr0.25)O3-δ (LSTZ) baseline. Furthermore, the ionic conductivity can be improved by another >70% via quenching, achieving >270% of the LSTZ. Notably, we demonstrate GB-enabled conductivity improvements via both promoting grain growth and altering GB structures through compositional designs and processing. In a broader perspective, this work suggests new routes for discovering and tailoring CCCs for energy storage and many other applications.
Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li0.375Sr0.4375Ta0.75Zr0.25O3
Tom Lee, Ji Qi, Chaitanya A. Gadre, Huaixun Huyan, Shu-Ting Ko, Yunxing Zuo, Chaojie Du, Jie Li, Toshihiro Aoki, Ruqian Wu, Jian Luo, Shyue Ping Ong, Xiaoqing Pan
Nature Communications, 2023, 14, 1, 1940
Abstract
Oxide solid electrolytes (OSEs) have the potential to achieve improved safety and energy density for lithium-ion batteries, but their high grain-boundary (GB) resistance generally is a bottleneck. In the well-studied perovskite oxide solid electrolyte, Li3xLa2/3-xTiO3 (LLTO), the ionic conductivity of grain
boundaries is about three orders of magnitude lower than that of the bulk. In
contrast, the related Li0.375Sr0.4375Ta0.75Zr0.25O3 (LSTZ0.75) perovskite exhibits low grain boundary resistance for reasons yet unknown. Here, we use aberration-corrected scanning transmission electron microscopy and spectroscopy, along with an active learning moment tensor potential, to reveal the
atomic scale structure and composition of LSTZ0.75 grain boundaries. Vibrational electron energy loss spectroscopy is applied for the first time to reveal atomically resolved vibrations at grain boundaries of LSTZ0.75 and to characterize the otherwise unmeasurable Li distribution therein. We find that Li depletion, which is a major reason for the low grain boundary ionic conductivity of LLTO, is absent for the grain boundaries of LSTZ0.75. Instead, the low grain boundary resistivity of LSTZ0.75 is attributed to the formation of a nanoscale defective cubic perovskite interfacial structure that contained
abundant vacancies. Our study provides new insights into the atomic scale mechanisms of low grain boundary resistivity.
Realizing Wide-Gamut Human-Centric Display Lighting with K3AlP3O9N:Eu2+
Shruti Hariyani, Xinxin Xing, Mahdi Amachraa, Jiming Bao, Shyue Ping Ong, Jakoah Brgoch
Advanced Optical Materials, 2023, 11, 8, 2202689
Abstract
Computers, televisions, and smartphones are revolutionized by the invention of InGaN blue light-emitting diode (LED) backlighting. Yet, continual exposure to the intense blue LED emission from these modern displays can cause insomnia and mood disorders. Developing “human-centric” backlighting that uses a violet-emitting LED chip and a trichromatic phosphor mixture to generate color images is one approach that addresses this problem. The challenge is finding a blue-emitting phosphor that possesses a sufficiently small Stokes’ shift to efficiently down-convert violet LED light and produce a narrow blue emission. This work reports a new oxynitride phosphor that meets this demand. K3AlP3O9N:Eu2+ exhibits an unexpectedly narrow (45 nm, 2206 cm−1), thermally robust, and efficient blue photoluminescence upon violet excitation. Computational modeling and temperature-dependent optical property measurements reveal that the narrow emission arises from a rare combination of preferential excitation and site-selective quenching. The resulting chromaticity coordinates of K3AlP3O9N:Eu2+ lie closer to the vertex of the Rec. 2020 than a blue LED chip and provides access to ≈10% more colors than a commercial tablet when combined with commercial red- and green-emitting phosphors. Alongside the wide gamut, tuning the emission from the violet LED and phosphor blend can reduce blue light emissions to produce next-generation, human-centric displays.
Intercalation Chemistry of the Disordered Rocksalt Li3V2O5 Anode from Cluster Expansions and Machine Learning Interatomic Potentials
Xingyu Guo, Chi Chen, Shyue Ping Ong
Chemistry of Materials, 2023, 35, 4, 1537–1546
Abstract
Disordered rocksalt (DRX) Li3V2O5 is a promising anode candidate for rechargeable lithium-ion batteries because of its low voltage, high rate capability, and good cycling stability. Herein, we present a comprehensive study of the intercalation chemistry of the DRX-Li3V2O5 anode using density functional theory (DFT) calculations combined with machine learning cluster expansions and interatomic potentials. The predicted voltage profile of the DRX Li3V2O5 anode at room temperature based on Monte Carlo simulations with a fitted cluster expansion model is in good agreement with experiments. In contrast to previous DFT results, we find that Li ions predominately intercalate into tetrahedral sites during charging, while a majority of Li and V ions at octahedral sites remain stable. In addition, molecular dynamics simulations with a fitted moment tensor potential attribute the fast-charging capability of DRX-Li3V2O5 to the facile diffusivity of Li+ via a tetrahedral−octahedral−tetrahedral pathway. We further suggest tuning the Li:V ratio as a means of trading off increased lithiation capacity and decreased anode voltage in this system. This work provides in-depth insights into the high-performance DRX-Li3V2O5 anode and paves the way for the discovery of other disordered anode materials.
Probing how Ti- and Nb-substitution affect the stability and improve the electrochemical performance of β- and ε-LiVOPO4
Marc F. V. Hidalgo, Isiksu Buyuker, Gabrielle E. Kamm, Zhuoying Zhu, Antonin Grenier, Mateusz J. Zuba, Zhi Deng, Yanxu Zong, Carol M. Kaplan, Natasha A. Chernova, Guangwen Zhou, Louis F. J. Piper, Shyue Ping Ong, Karena Chapman, Stanley Whittingham
Journal of Materials Chemistry A, 2023, 11, 2273-2290
Abstract
LiVOPO4 is a promising next-generation multi-electron cathode material, boasting a theoretical capacity of 305 mAh/g, significantly higher than any commercially used Li-ion battery cathode material. However, the material still faces several limitations, including the difficulty in attaining the full theoretical capacity at higher rates and capacity fade over several cycles. In this paper, we show that Ti- and Nb-substitution can be used to improve the thermal stability and electrochemical performance of LiVOPO4. We show through in-situ heating with XRD and a novel gradient heating technique that both Ti- and Nb-substitution cause β-LiVOPO4 to be stabilized relative to ε-LiVOPO4. This is due to transition-metal substitution, which increases the O-vacancy formation energies, pushing the β → ε transition to higher temperatures. We show that it is still possible to synthesize pure-phase ε-LiVOPO4 through the use of high temperatures to generate these O-vacancies. We show that even 1% of Ti- or Nb-substitution can improve the initial capacity and long term cycling capability of LiVOPO4 by improving the high-voltage capacity and reducing the capacity fade in both the high- and low-voltage regions. This is due to the an overall improved Li+ ion diffusion which is caused by an improved charge-transfer resistance during cycling.
Polaron-induced metal-to-insulator transition in vanadium oxides from density functional theory calculations
Jasleen Kaur, Manas Likhit Holekevi Chandrappa, Chi Chen, Shyue Ping Ong
Physical Review B, 2023, 107, 125162
Abstract
Vanadium oxides have been extensively studied as phase-change memory units in artificial synapses for neuromorphic computing due to their metal-insulator transitions (MIT) at or near room temperature. Recently, injection of charge carriers into vanadium oxides, e.g., via optically via a heterostructure, has been proposed as an alternative switching mechanism and also potentially as a means to tune the MIT temperature. In this study, we explore the formation of small polarons in the low temperature (LT) insulating phases for V3O5,VO2, and V2O3, and the barriers to their migration using density functional theory calculations. We find that V3O5 exhibits very low hole and electron polaron migration barriers (
Lithium dynamics at grain boundaries of β-Li3PS4 solid electrolyte
Randy Jalem, Manas Likhit Holekevi Chandrappa, Ji Qi, Yoshitaka Tateyama, Shyue Ping Ong
Energy Advances, 2023, 2, 2029-2041
Abstract
Lithium diffusivity at the grain boundaries of solid electrolytes (SEs) can strongly impact the final performance of all-solid-state Li ion batteries (SSLBs). β-Li3PS4 (β-LPS) is a promising SE due to its good formability and low processing cost, but its total Li ionic conductivity can vary by orders of magnitude depending on the synthesis and processing conditions. One of the possible sources for this variability, the GB contribution, is still poorly understood to date. In this study, we systematically investigate the Li ion transport in tilt and twist GBs as well as amorphous/crystal interfaces of β-LPS by performing large-scale molecular dynamics (MD) simulations with a highly accurate moment tensor interatomic potential (MTP). We find that the Li ion conductivities at the GBs and amorphous/crystal interfaces (10^−4–10^−3 S cm^−1) are 1–2 orders of magnitude higher than that in the bulk crystal (10^−5 S cm^−1). The Li ion diffusivity at twist GBs and amorphous/crystal interfaces shows no correlation with the degree of diffusion isotropy, while tilt GBs exhibit a negative correlation. Using topological data analysis, the Li pathway network in twist GBs and amorphous/crystal interfaces comprises persisting large Li ring sub-networks (nLi ≥ 5) that closely resemble those found in the bulk amorphous structure, whereas more smaller and short-lived Li ring sub-networks (nLi ≤ 5) are detected in tilt GBs and the bulk crystal. The concentration of persisting large Li ring sub-networks in the GB and amorphous/crystal interfaces is directly proportional to the degree of Li site disordering which in turn correlates with GB conductivity. Our findings provide useful insights that can guide the optimization of conductivity not only in β-LPS but also in other sulfide-type solid electrolytes through possible GB engineering.
Recent advances and outstanding challenges for machine learning interatomic potentials
Tsz Wai Ko, Shyue Ping Ong
Nature Computational Science, 2023, 10.1038/s43588-023-00561-9
Abstract
Machine learning interatomic potentials (MLIPs) enable materials simulations at extended length and time scales with near-ab initio accuracy. They have broad applications in the study and design of materials. Here, we discuss recent advances, challenges, and the outlook for MLIPs.
Multi-scale investigation of short-range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys
Hui Zheng, Lauren T. W. Fey, Xiang-Guo Li, Yong-Jie Hu, Liang Qi, Irene J. Beyerlein, Shyue Ping Ong
npj Computational Materials, 2023, 9, 89
Abstract
Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of short-range ordering (SRO) on dislocation glide in the MoNbTi and TaNbTi RMPEAs using a multi-scale modeling approach. Monte carlo/molecular dynamics simulations with a moment tensor potential show that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies (USFEs). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs and stress required for dislocation glide. The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, amount of hardening. Finally, we show how the morphology of an expanding dislocation loop is affected by the applied stress.
Oxygen-Loss-Induced Structural Degradation in ε-LiVOPO4
Hanlei Zhang, Hui Zhou, Zhi Deng, Langli Luo, Shyue Ping Ong, Chongmin Wang, Huolin Xin, M. Stanley Whittingham, Guangwen Zhou
ACS Applied Materials & Interfaces, 2022, 15, 1, 963–972
Abstract
The ε-LiVOPO4 cathode for Li-ion batteries has attracted wide attention with its multivalent electronic states and improved discharge capacity of over 300 mAh/g. Oxygen loss stands as a potential cause for structural degradations of the εLiVOPO4 cathode and its derivatives but has been barely studied. Through in situ environmental transmission electron microscopy, we probe lattice oxygen loss and the associated structural degradations by spatially and temporally resolving the atomicscale structural dynamics and phase transformation pathways in εLiVOPO4. We demonstrate that the mild oxygen loss at 400 °C induces a topotactic phase transformation of ε-LiVOPO4 → αLi3V2(PO4)3 in the particle surface via a nucleation and growth mechanism, leading to the formation of a core−shell configuration. The phase transformation can be reversed by switching to an oxidizing environment, in which the α-Li3V2(PO4)3 is reoxidized to εLiVOPO4. By contrast, oxygen loss at higher temperatures of 500 and 600 °C results in a high concentration of oxygen vacancies that subsequently induces irreversible structural damages including lattice amorphization and formation of nanocavities. This work illustrates the fundamental mechanisms governing the structural failure of oxide cathodes and underlines possible strategies to overcome such issues by exploiting environmental constraints.
A universal graph deep learning interatomic potential for the periodic table
Chi Chen, Shyue Ping Ong
Nature Computational Science, 2022, 2, 11, 718-728
Abstract
Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past ten years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. About 1.8 million materials from a screening of 31 million hypothetical crystal structures were identified to be potentially stable against existing Materials Project crystals based on M3GNet energies. Of the top 2,000 materials with the lowest energies above the convex hull, 1,578 were verified to be stable using density functional theory calculations. These results demonstrate a machine learningaccelerated pathway to the discovery of synthesizable materials with exceptional properties.
Synthetic control of structure and conduction properties in Na-Y-Zr-Cl solid electrolytes
Elias Sebti, Ji Qi, Peter M. Richardson, Phillip Ridley, Erik A. Wu, Swastika Banerjee, Raynald Giovine, Ashley Cronk, So-Yeon Ham, Ying Shirley Meng, Shyue Ping Ong, Raphaële J. Clément
Journal of Materials Chemistry A, 2022, 10, 40, 21565-21578
Abstract
In the development of low cost, sustainable, and energy-dense batteries, chloride-based compounds are promising catholyte materials for solid-state batteries owing to their high Na-ion conductivities and oxidative stabilities. The ability to further improve Na-ion conduction, however, requires an understanding of the impact of long-range and local structural features on transport in these systems. In this study, we leverage different synthesis methods to control polymorphism and cation disorder in Na–Y–Zr–Cl solid electrolytes and interrogate the impact on Na-ion conduction. We demonstrate the existence of a more conductive P21/n polymorph of Na2ZrCl6 formed upon ball milling. In Na3YCl6, the R3 polymorph is shown to be more conductive than its P21/n counterpart owing to the presence of
intrinsic vacancies and disorder on the Y sublattice. Transition metal ordering in the Na2.25Y0.25Zr0.75Cl6 composition strongly impacts Na-ion transport, where a greater mixing of Y3+ and Zr4+ on the transition metal sublattice facilitates ion migration through partial activation of Cl rotations at relevant temperatures. Overall, Na-ion transport sensitively depends on the phases and transition metal distributions stabilized during synthesis. These results are likely generalizable to other halide compositions and indicate that achieving control over the synthetic protocol and resultant structure is key in the pursuit of improved catholytes for high voltage solid-state sodium-ion batteries.
A flexible and scalable scheme for mixing computed formation energies from different levels of theory
Ryan S. Kingsbury, Andrew S. Rosen, Ayush S. Gupta, Jason M. Munro, Shyue Ping Ong, Anubhav Jain, Shyam Dwaraknath, Matthew K. Horton, Kristin A. Persson
npj Computational Materials, 2022, 8, 1, 195
Abstract
Abstract
Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory (DFT), which now contain millions of calculations at the generalized gradient approximation (GGA) level of theory. It is now feasible to carry out high-throughput calculations using more accurate methods, such as meta-GGA DFT; however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing (GGA) calculations. Instead, we propose here a general procedure by which higher-fidelity, low-coverage calculations (e.g., meta-GGA calculations for selected chemical systems) can be combined with lower-fidelity, high-coverage calculations (e.g., an existing database of GGA calculations) in a robust and scalable manner. We then use legacy PBE(+
U
) GGA calculations and new r
2
SCAN meta-GGA calculations from the Materials Project database to demonstrate that our scheme improves solid and aqueous phase stability predictions, and discuss practical considerations for its implementation.
An optoelectronic heterostructure for neuromorphic computing: CdS/V3O5
C. Adda, H. Navarro, J. Kaur, M.-H. Lee, C. Chen, M. Rozenberg, S. P. Ong, Ivan K. Schuller
Applied Physics Letters, 2022, 121, 4, 041901
Abstract
Nonvolatile resistive switching is one of the key phenomena for emerging applications in optoelectronics and neuromorphic computing. In most of the cases, an electric field is applied to a two terminal dielectric material device and leads to the formation of a low resistance filament due to ion migration. However, the stochastic nature of the ion migration can be an impediment for the device robustness and controllability, with uncontrolled variations of high and low resistance states or threshold voltages. Here, we report an optically induced resistive switching based on a CdS/V3O5 heterostructure which can overcome this issue. V3O5 is known to have a second order insulator to metal transition around Tc % 415 K, with an electrically induced threshold switching at room temperature. Upon illumination, the direct transfer of the photoinduced carriers from the CdS into V3O5 produces a nonvolatile resistive switching at room temperature. The initial high resistance can be recovered by reaching the high temperature metallic phase, i.e., temperatures above Tc. Interestingly, this resistive switching becomes volatile around the Tc. By locally manipulating the volatile and nonvolatile resistive switching using electric field and light, this system is a promising platform for hardware based neuromorphic computing implementations.
Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges
Axel Hoffmann, Shriram Ramanathan, Julie Grollier, Andrew D. Kent, Marcelo J. Rozenberg, Ivan K. Schuller, Oleg G. Shpyrko, Robert C. Dynes, Yeshaiahu Fainman, Alex Frano, Eric E. Fullerton, Giulia Galli, Vitaliy Lomakin, Shyue Ping Ong, Amanda K. Petford-Long, Jonathan A. Schuller, Mark D. Stiles, Yayoi Takamura, Yimei Zhu
APL Materials, 2022, 10, 7, 070904
Abstract
Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.
Electrochemically induced amorphous-to-rock-salt phase transformation in niobium oxide electrode for Li-ion batteries
Pete Barnes, Yunxing Zuo, Kiev Dixon, Dewen Hou, Sungsik Lee, Zhiyuan Ma, Justin G. Connell, Hua Zhou, Changjian Deng, Kassiopeia Smith, Eric Gabriel, Yuzi Liu, Olivia O. Maryon, Paul H. Davis, Haoyu Zhu, Yingge Du, Ji Qi, Zhuoying Zhu, Chi Chen, Zihua Zhu, Yadong Zhou, Paul J. Simmonds, Ariel E. Briggs, Darin Schwartz, Shyue Ping Ong, Hui Xiong
Nature Materials, 2022, 21, 795–803
Abstract
Intercalation-type metal oxides are promising negative electrode materials for safe rechargeable lithium-ion batteries due to the reduced risk of Li plating at low voltages. Nevertheless, their lower energy and power density along with cycling instability remain bottlenecks for their implementation, especially for fast-charging applications. Here, we report a nanostructured rock-salt Nb2O5 electrode formed through an amorphous-to-crystalline transformation during repeated electrochemical cycling with Li+. This electrode can reversibly cycle three lithiums per Nb2O5, corresponding to a capacity of 269 mAh g−1 at 20 mA g−1, and retains a capacity of 191 mAh g−1 at a high rate of 1 A g−1. It exhibits superb cycling stability with a capacity of 225 mAh g−1 at 200 mA g−1 for 400 cycles, and a Coulombic efficiency of 99.93%. We attribute the enhanced performance to the cubic rock-salt framework, which promotes low-energy migration paths. Our work suggests that inducing crystallization of amorphous nanomaterials through electrochemical cycling is a promising avenue for creating unconventional high-performance metal oxide electrode materials.
Local Environment Rigidity and the Evolution of Optical Properties in the Green-Emitting Phosphor Ba1-xSrxScO2F:Eu2+
Shruti Hariyani, Mahdi Amachraa, Mariam Khan, Shyue Ping Ong, Jakoah Brgoch
Journal of Materials Chemistry C, 2022, 10, 2955-2964
Abstract
Developing chemically and thermally stable, highly efficient green-emitting inorganic phosphors is a significant challenge in solid-state lighting. One accessible pathway for achieving green emission is by forming a solid solution with superior blue-emitting materials. In this work, we demonstrate that the cyan-emission (λem = 481 nm) of the BaScO2F:Eu2+ perovskite can be red-shifted by forming a solid solution following (Ba1-xSrx)0.98Eu0.02ScO2F (x = 0, 0.075, 0.15, 0.25, 0.33, 0.40). Although green emission is achieved (λem = 516 nm) as desired, the thermal quenching (TQ) resistance is reduced, and the photoluminescence quantum yield (PLQY) drops by 65%. Computation reveals the source of these changes. Surprisingly, a basic density functional theory analysis shows the gradual SrBa substitution has negligible effects on the band gap (Eg) energy, suggesting the activation energy barrier for the thermal ionization quenching remains unchanged, while the nearly constant Debye temperature indicates no loss of average structural rigidity to explain the decrease in the PLQY. Instead, temperature-dependent ab initio molecular dynamics (AIMD) simulations show that gradual changes of the Eu2+ ion’s local coordination environment rigidity are responsible for the drop in the observed TQ and PLQY. These results express the need to computationally analyze the local rare-earth environment as a function of temperature to understand the fundamental origin of optical properties in new inorganic phosphors.
MxLa1-xSiO2-yNz (M = Ca/Sr/Ba): Elucidating and Tuning the Structure and Eu2+ Local Environments to Develop Full-Visible Spectrum Phosphors
Mahdi Amachraa, Shuxing Li, Po-Yuan Huang, Ru-Shi Liu, Zhenbin Wang, Rong-Jun Xie, Shyue Ping Ong
Chemistry of Materials, 2022, 34, 9, 4039–4049
Abstract
The local environments of rare-earth activators have profound effects on the luminescent properties of phosphors. Here, we elucidate the crystal structure of the LaSiO2N phosphor host using a combination of density functional theory calculations and synchrotron Xray diffraction. We determine that LaSiO2N crystallizes in the monoclinic C2/c instead of the hexagonal P6̅c2 space group. To improve the luminescence performance, divalent cations M (M = Ca/Sr/Ba) were introduced into LaSiO2N to eliminate Eu3+. A family of apatite M1+xLa4−xSi3O13−x/2:Eu2+ (x ∼ 1.5, M = Ca/Sr/Ba) phosphors was further developed with unprecedented ultra-broadband (290 nm) emission spectra and excellent thermal stability. Detailed local environment investigations reveal that the formation of oxygen vacancies within and beyond the firstshell environment of Eu2+ is responsible for the redshift and broadening of the emission spectra via geometrical alteration of the Eu2+ local environment. This work provides new insights for understanding and optimizing the luminescence of rare-earth phosphors.
Efficient near-infrared phosphors discovered by parametrizing the Eu(II) 5d-to-4f energy gap
Shuxing Li, Mahdi Amachraa, Chi Chen, Le Wang, Zhenbin Wang, Shyue Ping Ong, Rong-Jun Xie
Matter, 2022, 5, 6, 1924-1936
Abstract
Summary Inorganic materials with rare-earth activators (e.g., Ce, Eu) exhibit broad 5d-to-4f emission spectra characterized by a strong host material dependency. Despite extensive research, the development of an efficient and near-infrared (NIR) 5d-to-4f emission remains elusive. Herein, we introduce key descriptors of the Eu(II)-host interactions and predict the in-crystal 5d-to-4f energy gap with a root-mean-square error of ca. 0.03 eV (7.0 nm). By incorporating this luminescence predictor into a high-throughput screening of 223 nitride materials in the Inorganic Crystal Structure Database, we identify and experimentally validate (Sr,Ba)3Li4Si2N6:Eu(II) with NIR emissions of λem = 800 ∼ 830 nm and high quantum efficiencies (QEs) of 30% ∼ 40%, leading to an NIR light power ∼3× superior to prevailing NIR emitters. The ultralong λem and high QE stem from a coordinated energy transfer and an optimized electronic delocalization around Eu(II). This work provides a cost-efficient computational approach for discovering phosphors with desired emissions.
Thermodynamics and Kinetics of the Cathode-Electrolyte Interface in All-Solid-State Li-S Batteries
Manas Likhit Holekevi Chandrappa, Ji Qi, Chi Chen, Swastika Banerjee, Shyue Ping Ong
Journal of the American Chemical Society, 2022, 144, 39, 18009–18022
Abstract
Lithium−sulfur batteries (LSBs) are among the most promising energy storage technologies due to the low cost and high abundance of S. However, the issue of polysulfide shuttling with its corresponding capacity fading is a major impediment to its commercialization. Replacing traditional liquid electrolytes with solidstate electrolytes (SEs) is a potential solution. Here, we present a comprehensive study of the thermodynamics and kinetics of the cathode−electrolyte interface in all-solid-state LSBs using density functional theory based calculations and a machine learning interatomic potential. We find that among the major solid electrolyte chemistries (oxides, sulfides, nitrides, and halides), sulfide SEs are generally predicted to be the most stable against the S8 cathode, while the other SE chemistries are predicted to be highly electrochemically unstable. If the use of other SE chemistries is desired for other reasons, several binary and ternary sulfides (e.g., LiAlS2, Sc2S3, Y2S3) are predicted to be excellent buffer layers. Finally, an accurate moment tensor potential to study the S8|β-Li3PS4 interface was developed using an active learning approach. Molecular dynamics (MD) simulations of large interface models (>1000s atoms) revealed that the most stable Li3PS4(100) surface tends to form interfaces with S8 with 2D channels and lower activation barriers for Li diffusion. These results provide critical new insights into the cathode−electrolyte interface design for next-generation all-solidstate LSBs.
Interfacial Stability of Layered LiNixMnyCo1–x–yO2 Cathodes with Sulfide Solid Electrolytes in All-Solid-State Rechargeable Lithium-Ion Batteries from First-Principles Calculations
Hideyuki Komatsu, Swastika Banerjee, Manas Likhit Holekevi Chandrappa, Ji Qi, Balachandran Radhakrishnan, Shigemasa Kuwata, Kazuyuki Sakamoto, Shyue Ping Ong
The Journal of Physical Chemistry C, 2022, 126, 41, 17482–17489
Abstract
Among the key impediments to the practical application of all-solid-state lithium-ion batteries are the reactions occurring at the interfaces between the electrode active material, the solid electrolyte, and conductive additives such as carbon. Here, we provide in-depth insights into the relationship between composition and interfacial stability with sulfide solid electrolytes for the layered LiNixMnyCo1−x−yO2 (NMC) cathodes in widespread commercial applications today using density functional theory calculations. We show that increasing the Ni content and, to a lesser extent, increasing the Co content, has the effect of increasing reactivity with the Li6PS5Cl (LPSCl) solid electrolyte. This suggests that current efforts to reduce the Co content in cathodes may compromise potential application in all-solid-state architectures. However, we also find that common SEI phases such as Li2CO3, surface phases such as NiO, and oxide buffer layers such as LiNbO3 generally exhibit only limited reactivity with either LiMO2 or LPSCl. Hence, these phases, formed either in operando or added during synthesis, can potentially serve as effective barriers against further reaction, provided a uniform coating can be achieved.
Role of Critical Oxygen Concentration in the β-Li3PS4-xOx Solid Electrolyte
Swastika Banerjee, Manas Likhit Holekevi Chandrappa, Shyue Ping Ong
ACS Applied Energy Materials, 2022, 5, 1, 35–41
Abstract
Lithium superionic conductors are the critical enabling component for next-generation all-solid lithium-ion batteries. In particular, the β polymorph of Li3PS4 has attracted major interest due to its combination of excellent ionic conductivity and passivating interfacial stability with Li. In this work, we systematically investigated the effect of oxygenation in β-Li3PS4 to further enhance its ionic conductivity and electrochemical stability using density functional theory calculations and ab initio molecular dynamics simulations. We predict that a maximum ionic conductivity of 1.52 mS cm−1 (and minimum activation energy) can be achieved at x = 0.25 in Li3PS4-xOx which is about 7 times higher than that of β-Li3PS4. This increase in ionic conductivity can be attributed to the flattening of the potential energy surface due to the diversification of the Li chemical environments by the S−O mixed-anionic framework, resulting in a change from quasi-2D to 3D Li diffusion. We highlight that the spatial localization of the electrostatic potential is a qualitative descriptor to assess the migration barrier of the charge carrier in the S−O mixed framework. These microscopic analyses shed light on the role of critical oxygen concentration to tune the rate-performance of mixed-anion lithium superionic conductors.
All‐Electric Nonassociative Learning in Nickel Oxide
Sandip Mondal, Zhen Zhang, A. N. M. Nafiul Islam, Robert Andrawis, Sampath Gamage, Neda Alsadat Aghamiri, Qi Wang, Hua Zhou, Fanny Rodolakis, Richard Tran, Jasleen Kaur, Chi Chen, Shyue Ping Ong, Abhronil Sengupta, Yohannes Abate, Kaushik Roy, Shriram Ramanathan
Advanced Intelligent Systems, 2022, 4, 10, 2200069
Abstract
Habituation and sensitization represent nonassociative learning mechanisms in both non-neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular-like learning in oxygen-deficient NiOx devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiOx under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next-generation neuroinspired computing hardware.
A Universal Machine Learning Model for Elemental Grain Boundary Energies
Weike Ye, Hui Zheng, Chi Chen, Shyue Ping Ong
Scripta Materialia, 2022, 218, 114803
Abstract
The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small Σ (Σ
Recent advances and applications of deep learning methods in materials science
Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth Holm, Shyue Ping Ong, Chris Wolverton
npj Computational Materials, 2022, 8, 1, 59
Abstract
Abstract
Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.
Emergence of near-boundary segregation zones in face-centered cubic multiprincipal element alloys
Megan J. McCarthy, Hui Zheng, Diran Apelian, William J. Bowman, Horst Hahn, Jian Luo, Shyue Ping Ong, Xiaoqing Pan, Timothy J. Rupert
Physical Review Materials, 2021, 5, 11, 113601
Abstract
Grain boundaries have been shown to dramatically influence the behavior of relatively simple materials such as monatomic metals and binary alloys. The increased chemical complexity associated with multiprincipal element alloys is hypothesized to lead to unique grain boundary phenomena. To explore the relationship between grain boundary structure and chemistry in these materials, hybrid molecular dynamics/Monte Carlo simulations of a faceted Sigma 11 tilt boundary, chosen to sample both high- and low-energy boundary configurations, are performed in face-centered cubic (fcc) CrFeCoNiCu and CrFeCoNi equiatomic alloys. Unexpected enrichment of Fe is discovered in the fcc regions adjacent to the interface and found to be correlated with a structurally distinct region of reduced atomic volume. Comparison with the boundary of the same type in monatomic Cu demonstrates that altered near-boundary regions exist in simpler systems as well, with the chemical complexity of the multiprincipal element alloys highlighting its existence and importance.
Proton distribution visualization in perovskite nickelate devices utilizing nanofocused x rays
Ivan A. Zaluzhnyy, Peter O. Sprau, Richard Tran, Qi Wang, Hai-Tian Zhang, Zhen Zhang, Tae Joon Park, Nelson Hua, Boyan Stoychev, Mathew J. Cherukara, Martin V. Holt, Evgeny Nazaretski, Xiaojing Huang, Hanfei Yan, Ajith Pattammattel, Yong S. Chu, Shyue Ping Ong, Shriram Ramanathan, Oleg G. Shpyrko, Alex Frano
Physical Review Materials, 2021, 5, 9, 095003
Abstract
We use a 30-nm x-ray beam to study the spatially resolved properties of a SmNiO 3 -based nanodevice that is doped with protons. The x-ray absorption spectra supported by density-functional theory simulations show partial reduction of nickel valence in the region with high proton concentration, which leads to the insulating behavior. Concurrently, x-ray diffraction reveals only a small lattice distortion in the doped regions. Together, our results directly show that the knob which proton doping modifies is the electronic valency and not the crystal lattice. The studies are relevant to ongoing efforts to disentangle structural and electronic effects across metal-insulator phase transitions in correlated oxides.
Morphology Control of Tantalum Carbide Nanoparticles through Dopant Additions
Tianqi Ren, Richard Tran, Sebastian Lee, Aric Bandera, Manuel Herrera, Xiang-Guo Li, Shyue Ping Ong, Olivia A. Graeve
The Journal of Physical Chemistry C, 2021, 125, 19, 10665-10675
Abstract
The control of powder morphology in metals and ceramics is of critical importance in applications such as catalysis and chemical sensing whereby specific crystal facets better facilitate chemical reactions. In response to this challenge, we present a combined experimental and computational approach that examines the principles behind dopant-induced crystallographic faceting in nanoparticles. We base our study on nanoparticles of tantalum carbide doped with nickel, iron, cobalt, niobium, and titanium and observe a very significant transition from round/irregular particle shapes to cubes and cuboctahedrons upon the addition of transition metal dopants. The presence of the dopants, which segregate toward the surface of the particles, results in atomic orbital hybridization, causing a significant decrease of up to 0.13 eV·Å−2 in the surface energy of the (100) facets, thus providing the driving force for the formation of nanocubes with exposed (100) surfaces. These principles can be generalized to other ceramics and serve as guidance for the optimized control of shape in powders. For example, if one seeks to produce highly faceted V-, Hf-, or Zr-carbide nanoparticles, doping strategies reported here can be applied. Other elements may also be effective in changing the growth habits of crystals based on surface segregation and dopant−host atomic orbital hybridization.
Metal-insulator transition in V2O3 with intrinsic defects
Richard Tran, Xiang-Guo Li, Yoav Kalcheim, Ivan K. Schuller, Shyue Ping Ong
Physical Review B, 2021, 103, 7, 075134
Abstract
Vanadium sesquioxide (V2O3) is a Mott insulator exhibiting a temperature-dependent metal-insulator transition (MIT) at 165 K accompanied by both a magnetic and structural transition. Although it is expected to be a metal under conventional band theory, electron interactions at low temperature cause it to behave like an insulator, making it difficult to accurately model its electronic properties with standard ab initio methods. As such, accurate theoretical assessments of the MIT with point defects requires special attention to the type of functionals used. In this study, we conclude that the PBE + U functional provides the best compromise between accuracy and efficiency in calculating the properties related to the MIT between low-temperature and high-temperature V2O3. We use this functional to explore the various influences that intrinsic point defects will have on the MIT in V2O3.
Learning properties of ordered and disordered materials from multi-fidelity data
Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Shyue Ping Ong
Nature Computational Science, 2021, 1, 46-53
Abstract
Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new paradigm to provide rapid predictions of materials properties, their practical utility is limited by the scarcity of high-fidelity data. Here, we develop multi-fidelity graph networks as a universal approach to achieve accurate predictions of materials properties with small data sizes. As a proof of concept, we show that the inclusion of low-fidelity Perdew–Burke–Ernzerhof band gaps greatly enhances the resolution of latent structural features in materials graphs, leading to a 22–45% decrease in the mean absolute errors of experimental band gap predictions. We further demonstrate that learned elemental embeddings in materials graph networks provide a natural approach to model disorder in materials, addressing a fundamental gap in the computational prediction of materials properties.
Atomistic simulations of dislocation mobility in refractory high-entropy alloys and the effect of chemical short-range order
Sheng Yin, Yunxing Zuo, Anas Abu-Odeh, Hui Zheng, Xiang-Guo Li, Jun Ding, Shyue Ping Ong, Mark Asta, Robert O. Ritchie
Nature Communications, 2021, 12, 1, 4873
Abstract
Abstract
Refractory high-entropy alloys (RHEAs) are designed for high elevated-temperature strength, with both edge and screw dislocations playing an important role for plastic deformation. However, they can also display a significant energetic driving force for chemical short-range ordering (SRO). Here, we investigate mechanisms underlying the mobilities of screw and edge dislocations in the body-centered cubic MoNbTaW RHEA over a wide temperature range using extensive molecular dynamics simulations based on a highly-accurate machine-learning interatomic potential. Further, we specifically evaluate how these mechanisms are affected by the presence of SRO. The mobility of edge dislocations is found to be enhanced by the presence of SRO, whereas the rate of double-kink nucleation in the motion of screw dislocations is reduced, although this influence of SRO appears to be attenuated at increasing temperature. Independent of the presence of SRO, a cross-slip locking mechanism is observed for the motion of screws, which provides for extra strengthening for refractory high-entropy alloy system.
AtomSets as a hierarchical transfer learning framework for small and large materials datasets
Chi Chen, Shyue Ping Ong
npj Computational Materials, 2021, 7, 1, 173
Abstract
Predicting properties from a material’s composition or structure is of great interest for materials design. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we develop the AtomSets framework, which utilizes universal compositional and structural descriptors extracted from pre-trained graph network deep learning models with standard multi-layer perceptrons to achieve consistently high model accuracy for both small compositional data (130,000). The AtomSets models show lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits. They also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework can potentially accelerate machine learning-assisted materials design and discovery with less data restriction.
Database of ab initio L-edge X-ray absorption near edge structure
Yiming Chen, Chi Chen, Chen Zheng, Shyam Dwaraknath, Matthew K. Horton, Jordi Cabana, John Rehr, John Vinson, Alan Dozier, Joshua J. Kas, Kristin A. Persson, Shyue Ping Ong
Scientific Data, 2021, 8, 1, 153
Abstract
The L-edge X-ray Absorption Near Edge Structure (XANES) is widely used in the characterization of transition metal compounds. Here, we report the development of a database of computed L-edge XANES using the multiple scattering theory-based FEFF9 code. The initial release of the database contains more than 140,000 L-edge spectra for more than 22,000 structures generated using a high-throughput computational workflow. The data is disseminated through the Materials Project and addresses a critical need for L-edge XANES spectra among the research community.
Inherent stochasticity during insulator-metal transition in VO2
Shaobo Cheng, Min-Han Lee, Richard Tran, Yin Shi, Xing Li, Henry Navarro, Coline Adda, Qingping Meng, Long-Qing Chen, R C Dynes, Shyue Ping Ong, Ivan K Schuller, Yimei Zhu
Proceedings of the National Academy of Science USA, 2021, 118, 37, e2105895118
Abstract
Vanadium dioxide (VO2 ), which exhibits a near-room-temperature insulator–metal transition, has great potential in applications of neuromorphic computing devices. Although its volatile switching property, which could emulate neuron spiking, has been studied widely, nanoscale studies of the structural stochasticity across the phase transition are still lacking. In this study, using in situ transmission electron microscopy and ex situ resistive switching measurement, we successfully characterized the structural phase transition between monoclinic and rutile VO 2 at local areas in planar VO2 /TiO 2 device configuration under external biasing. After each resistive switching, different VO 2 monoclinic crystal orientations are observed, forming different equilibrium states. We have evaluated a statistical cycle-to-cycle variation, demonstrated a stochastic nature of the volatile resistive switching, and presented an approach to study in-plane structural anisotropy. Our microscopic studies move a big step forward toward understanding the volatile switching mechanisms and the related applications of VO 2 as the key material of neuromorphic computing.
A framework for quantifying uncertainty in DFT energy corrections
Amanda Wang, Ryan Kingsbury, Matthew McDermott, Matthew Horton, Anubhav Jain, Shyue Ping Ong, Shyam Dwaraknath, Kristin A. Persson
Scientific Reports, 2021, 11, 1, 15496
Abstract
Abstract
In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability.
Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors
J. Qi, S. Banerjee, Y. Zuo, C. Chen, Z. Zhu, M.L. Holekevi Chandrappa, X. Li, S.P. Ong
Materials Today Physics, 2021, 21, 100463
Abstract
Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. While ab initio molecular dynamics have been extensively applied to study these materials, there are often large discrepancies between predicted and experimentally measured ionic conductivities and activation energies due to the high temperatures and short time scales of such simulations. Here, we present a strategy to bridge this gap using moment tensor potentials (MTPs). We show that MTPs trained on energies and forces computed using the van der Waals optB88 functional yield much more accurate lattice parameters, which in turn leads to accurate prediction of ionic conductivities and activation energies for the Li0$33La0$56TiO3, Li3YCl6 and Li7P3S11 LSCs. NPT MD simulations using the optB88 MTPs also reveal that all three LSCs undergo a transition between two quasi-linear Arrhenius regimes at relatively low temperatures. This transition can be traced to an increase in the number and diversity of diffusion pathways, in some cases with a change in the dimensionality of diffusion. This work presents not only an approach to develop high accuracy MTPs, but also outlines the diffusion characteristics for LSCs which is otherwise inaccessible through ab initio computation.
Correlated Octahedral Rotation and Organic Cation Reorientation Assist Halide Ion Migration in Lead Halide Perovskites
Manas Likhit Holekevi Chandrappa, Zhuoying Zhu, David P Fenning, Shyue Ping Ong
Chemistry of Materials, 2021, 33, 12, 4672–4678
Abstract
Increasing the stability of lead halide perovskites (LHPs) is critical for their practical application in solar cells and other technologies. Halide ion migration is one of the main contributors to instability and hysteresis in LHP solar cells. Here, we employ a series of Gedankenexperiments to quantitatively establish the correlated effects of the A site cation motion, H bonding strength, and octahedral rotation on halide ion migration in APbBr3 (A = Cs or methylammonium/MA) LHPs. We find that in cubic CsPbBr3, the increase of PbBr6 octahedra rotation/tilting during ion migration lowers the halide ion migration barrier by at least 100 meV compared to the orthorhombic phase. In MAPbBr3, we show that halide ion migration is also assisted by MA cation rotation to re-establish H bonding, resulting in lower halide migration barriers. These results suggest that “locking” the organic cation via chemical and processing means can help mitigate halide migration-induced instability and reduced hysteresis in LHP solar cells.
Tunable Lithium-Ion Transport in Mixed-Halide Argyrodites Li6-xPS5-xClBrx: An Unusual Compositional Space
Sawankumar V Patel, Swastika Banerjee, Haoyu Liu, Pengbo Wang, Po-Hsiu Chien, Xuyong Feng, Jue Liu, Shyue Ping Ong, Yan-Yan Hu
Chemistry of Materials, 2021, 33, 4, 1435–1443
Abstract
Argyrodites, with fast lithium-ion conduction, are promising for applications in rechargeable solid-state lithium-ion batteries. We report a new compositional space of argyrodite superionic conductors, Li6−xPS5−xClBrx [0 ≤ x ≤ 0.8], with a remarkably high ionic conductivity of 24 mS/cm at 25 °C for Li5.3PS4.3ClBr0.7. In addition, the extremely low lithium migration barrier of 0.155 eV makes Li5.3PS4.3ClBr0.7 highly promising for low-temperature operation. Average and local structure analyses reveal that bromination (x > 0) leads to (i) retention of the parent Li6PS5Cl structure for a wide range of x in Li6−xPS5−xClBrx (0 ≤ x ≤ 0.7), (ii) co-occupancy of Cl-, Br- , and S2- at 4a/4d sites, and (iii) gradually increased Li+-ion dynamics, eventually yielding a “liquid-like” Li-sublattice with a flattened energy landscape when x approaches 0.7. In addition, the diversity of anion species and Li-deficiency in halogen-rich Li6−xPS5−xClBrx induce hypercoordination and coordination entropy for the Li-sublattice, also leading to enhanced Li+-ion transport in Li6−xPS5−xClBrx. This study demonstrates that mixed-anion framework can help stabilize highly conductive structures in a compositional space otherwise unstable with lower anion diversity.
Design Principles for Cation‐Mixed Sodium Solid Electrolytes
Zhuoying Zhu, Hanmei Tang, Ji Qi, Xiang-Guo Li, Shyue Ping Ong
Adv. Energy Mater., 2021, 11, 7, 2003196
Abstract
All‐solid‐state sodium‐ion batteries are highly promising for next generation grid energy storage with improved safety. Among the known sodium superionic conductors, the Na3PnS4 family and the recently discovered Na11Sn2PnS12 (Pn = P, Sb) have garnered major interest due to their extremely high ionic conductivities. In this work, comprehensive investigation of the Na3PnS4‐Na4TtS4 (Pn = P/As/Sb, Tt = Si/Ge/Sn) phase space of superionic conductors using density functional theory calculations, as well as AIMD simulations on the promising new Na11Sn2PnS12 (Pn=P/As/Sb) structures are presented. Crucial design rules on the effect of cation mixing are extracted on relative phase stability, electrochemical stability, moisture stability, and ionic conductivity. In particular, it is shown that while larger cations can substantially improve the ionic conductivity and moisture stability in these structures, there is an inherent trade‐off in terms of electrochemical stability. Na11Sn2AsS12 is also identified as a hitherto unexplored stable sodium superionic conductor with higher Na+ conductivity and better moisture stability than the Na11Sn2PS12 and Na11Sn2SbS12 phases already reported experimentally.
A Stable Cathode-Solid Electrolyte Composite for High-Voltage, Long-Cycle-Life Solid-State Sodium-Ion Batteries
Erik A. Wu, Swastika Banerjee, Hanmei Tang, Peter M. Richardson, Jean-Marie Doux, Ji Qi, Zhuoying Zhu, Antonin Grenier, Yixuan Li, Enyue Zhao, Grayson Deysher, Elias Sebti, Han Nguyen, Ryan Stephens, Guy Verbist, Karena W. Chapman, Raphaële J. Clément, Abhik Banerjee, Ying Shirley Meng, Shyue Ping Ong
Nature Communications, 2021, 12, 1, 1256
Abstract
Rechargeable solid-state sodium-ion batteries (SSSBs) hold great promise for safer and more energy-dense energy storage. However, the poor electrochemical stability between current sulfide-based solid electrolytes and high-voltage oxide cathodes has limited their long-term cycling performance and practicality. Here, we report the discovery of the ion conductor Na3-xY1-xZrxCl6 (NYZC) that is both electrochemically stable (up to 3.8 V vs. Na/Na+) and chemically compatible with oxide cathodes. Its high ionic conductivity of 6.6 × 10−5 S cm−1 at ambient temperature, several orders of magnitude higher than oxide coatings, is attributed to abundant Na vacancies and cooperative MCl6 rotation, resulting in an extremely low interfacial impedance. A SSSB comprising a NaCrO2 + NYZC composite cathode, Na3PS4 electrolyte, and Na-Sn anode exhibits an exceptional first-cycle Coulombic efficiency of 97.1% at room temperature and can cycle over 1000 cycles with 89.3% capacity retention at 40 °C. These findings highlight the immense potential of halides for SSSB applications.
Accelerating materials discovery with Bayesian optimization and graph deep learning
Yunxing Zuo, Mingde Qin, Chi Chen, Weike Ye, Xiangguo Li, Jian Luo, Shyue Ping Ong
Materials Today, 2021, 51, 126-135
Abstract
Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive density functional theory (DFT) calculations, which limits ML-based exploration to either known crystals or a small number of hypothetical crystals. Here, we demonstrate that the application of Bayesian optimization with symmetry constraints using a graph deep learning energy model can be used to perform “DFT-free” relaxations of crystal structures. Using this approach to significantly improve the accuracy of ML-predicted formation energies and elastic moduli of hypothetical crystals, two novel ultra-incompressible hard MoWC 2 (P6 3 =mmc) and ReWB (Pca2 1 ) were identified and successfully synthesized via in situ reactive spark plasma sintering from screening 399,960 transition metal borides and carbides. This work addresses a critical bottleneck to accurate property predictions for hypothetical materials, paving the way to ML-accelerated discovery of new materials with exceptional properties.
Vanadyl Phosphates AxVOPO4 (A = Li, Na, K) as Multielectron Cathodes for Alkali‐Ion Batteries
Natasha A. Chernova, Marc Francis V. Hidalgo, Carol Kaplan, Krystal Lee, Isiksu Buyuker, Carrie Siu, Bohua Wen, Jia Ding, Mateusz Zuba, Kamila M. Wiaderek, Ieuan D. Seymour, Sylvia Britto, Louis F. J. Piper, Shyue Ping Ong, Karena W. Chapman, Clare P. Grey, M. Stanley Whittingham
Advanced Energy Materials, 2020, 10, 47, 2002638
Abstract
Vanadyl phosphates comprise a class of multielectron cathode materials capable of cycling two Li+ , about 1.66 Na+ , and some K + ions per redox center. In this review, structures, thermodynamic stabilities, and ion diffusion kinetics of various Ax VOPO 4 (A = Li, Na, K, NH4 ) polymorphs are discussed. Both the experimental data and first-principle calculations indicate kinetic limitations for alkali metal ions cycling, especially between for 0 ≤ x ≤ 1, and metastability of phases with x > 1. This creates challenges for multiple-ion cycling, as the slow kinetics call for nanosized particles, which being metastable and reactive with organic electrolytes are prone to side reactions. Thus, various synthesis approaches, surface coating, and transition metal ion substitution strategies are discussed here as possible ways to stabilize Ax VOPO 4 structures and improve alkali metal ion diffusion. The role of advanced characterization techniques, such as X-ray absorption spectroscopy, diffraction, pair distribution function analysis and7 Li and31 P NMR, in understanding the reaction mechanism from both structural and electronic points of view is emphasized.
A disordered rock salt anode for fast-charging lithium-ion batteries
Haodong Liu, Zhuoying Zhu, Qizhang Yan, Sicen Yu, Xin He, Yan Chen, Rui Zhang, Lu Ma, Tongchao Liu, Matthew Li, Ruoqian Lin, Yiming Chen, Yejing Li, Xing Xing, Yoonjung Choi, Lucy Gao, Helen Sung-yun Cho, Ke An, Jun Feng, Robert Kostecki, Khalil Amine, Tianpin Wu, Jun Lu, Huolin L. Xin, Shyue Ping Ong, Ping Liu
Nature, 2020, 585, 63-67
Abstract
Rechargeable lithium-ion batteries with high energy density that can be safely charged and discharged at high rates are desirable for electrified transportation and other applications. However, the sub-optimal intercalation potentials of current anodes result in a trade-of between energy density, power and safety. Here we report that disordered rock salt Li3+xV2O5 can be used as a fast-charging anode that can reversibly cycle two lithium ions at an average voltage of about 0.6 volts versus a Li/Li+ reference electrode. The increased potential compared to graphite reduces the likelihood of lithium metal plating if proper charging controls are used, alleviating a major safety concern (short-circuiting related to Li dendrite growth). In addition, a lithium-ion battery with a disordered rock salt Li3V2O5 anode yields a cell voltage much higher than does a battery using a commercial fast-charging lithium titanate anode or
other intercalation anode candidates (Li3VO4 and LiV0.5Ti0.5S2). Further, disordered rock salt Li3V2O5 can perform over 1,000 charge–discharge cycles with negligible capacity decay and exhibits exceptional rate capability, delivering over 40 per cent of its capacity in 20 seconds. We attribute the low voltage and high rate capability of
disordered rock salt Li3V2O5 to a redistributive lithium intercalation mechanism with low energy barriers revealed via ab initio calculations. This low-potential, high-rate intercalation reaction can be used to identify other metal oxide anodes for fast-charging, long-life lithium-ion batteries.
Design Principles for Aqueous Na-ion Battery Cathodes
Xingyu Guo, Zhenbin Wang, Zhi Deng, Bo Wang, Xi Chen, Shyue Ping Ong
Chemistry of Materials, 2020, 32, 16, 6875--6885
Abstract
Here, we develop design rules for aqueous sodium-ion battery cathodes through a comprehensive density functional theory study of the working potential and aqueous stability of known cathode materials. These design rules were applied in a high-throughput screening of Na-ion battery cathode materials for application in aqueous electrolytes. Five promising cathode materials, NASICON-Na3Fe2(PO4)3, Na2FePO4F, Na3FeCO3PO4, alluadite-Na2Fe3(PO4)3, and Na3MnCO3PO4, were identified as hitherto-unexplored aqueous sodium-ion battery cathodes, with high voltage, good capacity, high stability in aqueous environments, and facile Na-ion migration. These findings pave the way for practical cathode development for large-scale energy-storage systems based on aqueous Na-ion battery chemistry.
Predicting Thermal Quenching in Inorganic Phosphors
Mahdi Amachraa, Zhenbin Wang, Chi Chen, Shruti Hariyani, Hanmei Tang, Jakoah Brgoch, Shyue Ping Ong
Chemistry of Materials, 2020, 32, 14, 6256–6265
Abstract
Phosphor-converted light emitting diodes (LEDs) are a highly efficient form of solid-state lighting. A key performance metric of a phosphor is its thermal quenching (TQ), which is the percentage loss of emission at elevated temperatures during operation. In this work, we unify the two prevailing theoriesthe crossover and thermal ionization mechanismsinto a single predictive model for TQ. Using ab initio molecular dynamics (AIMD) simulations, we demonstrate for the first time that TQ under the crossover mechanism is related to the local environment stability of the activator. Further, by accounting for the effect of the crystal field on the thermal ionization barrier, we show that a unified model can predict the experimental TQ in 29 known phosphors to within a root-mean-square error of ∼3.1−7.6%. Finally, we propose an efficient topological approach to rapidly screen vast chemical spaces for the discovery of novel, thermally robust phosphors.
Cation-Size Mismatch as a Design Principle for Enhancing the Efficiency of Garnet Phosphors
Yoon Hwa Kim, Ha Jun Kim, Shyue Ping Ong, Zhenbin Wang, Won Bin Im
Chemistry of Materials, 2020, 32, 7, 3097-3108
Abstract
In this study, we report on the development of a new garnet phosphor with enhanced optical properties and cost reduction. Samples were prepared using the solid-solution method, in which the chemical unit and substitutions with cation-size mismatch were combined. Solid solutions between two garnet structure compounds, green phosphor Lu3Al5O12:Ce3+ (LuAG:Ce3+) and orange phosphor Lu2CaMg2Si3O12:Ce3+ (Lu3−xCaxAl2−2xMg2xAl3−3xSi3xO12:Ce3+), constituted the complete solid-solution range x (x = 0−1). The crystal structures of all the compounds were discerned through Rietveld refinement based on the X-ray diffraction patterns. The unique occupancy of {Lu/Ca}, [Al/Mg], (Al/Si), and O atoms in the solid-solution samples was identified. Optical properties were classified in terms of the excitation and emission spectra, quantum yield, and temperature-dependent photoluminescence intensity. To investigate the relationship between the structural and optical changes, Ba2+ ions (employed for cation-size mismatch) were substituted into dodecahedral and octahedral sites at various concentrations. Finally, we report the development of a new green garnet phosphor via the use of a solid-solution design and cation-size mismatch, the emission intensity of which was measured 116% higher than that of commercial LuAG:Ce3+.
Rechargeable Alkali-Ion Battery Materials: Theory and Computation
Anton Van der Ven, Zhi Deng, Swastika Banerjee, Shyue Ping Ong
Chemical Reviews, 2020, 120, 14, 6977–7019
Abstract
Since its development in the 1970s, the rechargeable alkali-ion battery has proven to be a truly transformative technology, providing portable energy storage for devices ranging from small portable electronics to sizable electric vehicles. Here, we present a review of modern theoretical and computational approaches to the study and design of rechargeable alkali-ion battery materials. Starting from fundamental thermodynamics and kinetics phenomenological equations, we rigorously derive the theoretical relationships for key battery properties, such as voltage, capacity, alkali diffusivity, and other electrochemically relevant computable quantities. We then present an overview of computational techniques for the study of rechargeable alkali-ion battery materials, followed by a critical review of the literature applying these techniques to yield crucial insights into battery operation and performance. Finally, we provide perspectives on outstanding challenges and opportunities in the theory and computation of rechargeable alkali-ion battery materials.
A Critical Review of Machine Learning of Energy Materials
Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, Shyue Ping Ong
Advanced Energy Materials, 2020, 10, 8, 1903242
Abstract
Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in-depth review of the application of ML to energy materials, including rechargeable alkali-ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.
Performance and Cost Assessment of Machine Learning Interatomic Potentials
Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
The Journal of Physical Chemistry A, 2020, 124, 4, 731-745
Abstract
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors - atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors - using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.
Grain Boundary Properties of Elemental Metals
Hui Zheng, Xiang-Guo Li, Richard Tran, Chi Chen, Matthew Horton, Donny Winston, Kristin Aslaug Persson, Shyue Ping Ong
Acta Materialia, 2020, 186, 40-49
Abstract
The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional theory calculations workflow to construct the Grain Boundary Database (GBDB), the largest database of DFT-computed grain boundary properties to date. The database currently encompasses 327 GBs of 58 elemental metals, including 10 common twist or symmetric tilt GBs for body-centered cubic (bcc) and face-centered cubic (fcc) systems and the 7 [0001] twist GB for hexagonal close-packed (hcp) systems. In particular, we demonstrate a novel scaled-structural template approach for HT GB calculations, which reduces the computational cost of converging GB structures by a factor of ~ 3-6. The grain boundary energies and work of separation are rigorously validated against previous experimental and computational data. Using this large GB dataset, we develop an improved predictive model for the GB energy of different elements based on the cohesive energy and shear modulus. The open GBDB represents a significant step forward in the availability of first principles GB properties, which we believe would help guide the future design of polycrystalline materials.
Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy
Xiang-Guo Li, Chi Chen, Hui Zheng, Yunxing Zuo, Shyue Ping Ong
npj Computational Materials, 2020, 6, 1, 70
Abstract
Refractory multi-principal element alloys (MPEAs) have exceptional mechanical properties, including high strength-to-weight ratio and fracture toughness, at high temperatures. Here we elucidate the complex interplay between segregation, short-range order, and strengthening in the NbMoTaW MPEA through atomistic simulations with a highly accurate machine learning interatomic potential. In the single crystal MPEA, we find greatly reduced anisotropy in the critically resolved shear stress between screw and edge dislocations compared to the elemental metals. In the polycrystalline MPEA, we demonstrate that thermodynamically driven Nb segregation to the grain boundaries (GBs) and W enrichment within the grains intensifies the observed short-range order (SRO). The increased GB stability due to Nb enrichment reduces the von Mises strain, resulting in higher strength than a random solid solution MPEA. These results highlight the need to simultaneously tune GB composition and bulk SRO to tailor the mechanical properties of MPEAs.
Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure
Chen Zheng, Chi Chen, Yiming Chen, Shyue Ping Ong
Patterns, 2020, 1, 2, 100013
Abstract
Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge structure (XANES) spectra can identify the main atomic coordination environment with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine-learning models. In a departure from prior works, the coordination environment is described as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1 to 12. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. A drop-variable feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification.
Multiprincipal Component P2-Na0.6(Ti0.2Mn0.2Co0.2Ni0.2Ru0.2)O2 as a High-Rate Cathode for Sodium-Ion Batteries
Lufeng Yang, Chi Chen, Shan Xiong, Chen Zheng, Pan Liu, Yifan Ma, Wenqian Xu, Yuanzhi Tang
JACS Au, 2020, 1, 1, 98–107
Abstract
Mixing transition metal cations in nearly equiatomic proportions in layered oxide cathode materials is a new strategy for improving the performances of Na-ion batteries. The mixing of cations not only offers entropic stabilization of the crystal structure but also benefits the diffusion of Na ions with tuned diffusion activation energy barriers. In light of this strategy, a high-rate Na0.6(Ti0.2Mn0.2Co0.2Ni0.2Ru0.2)O2 cathode was designed, synthesized, and investigated, combining graph-based deep learning calculations and complementary experimental characterizations. This new cathode material delivers high discharge capacities of 164 mA g−1 at 0.1 C and 68 mAh g−1 at a very high rate of 86 C, demonstrating an outstanding high rate capability. Ex situ and operando synchrotron X-ray diffraction were used to reveal the detailed structural evolution of the cathode upon cycling. Using the climbing-image nudged elastic-band calculation and Ab initio molecular dynamics simulations, we show that the optimal transition metal composition enables a percolating network of low barrier pathways for fast, macroscopic Na diffusion, resulting in the observed high rate performance.
Jahn-Teller distortion-driven robust blue-light-emitting perovskite nanoplatelets
G. Krishnamurthy Grandhi, H.C. Manas Likhit, Shyue Ping Ong, Won Bin Im
Applied Materials Today, 2020, 20, 100668
Abstract
Blue-light-emitting and quantum-confined two-dimensional CsPbBr3 perovskite nanoplatelets (NPLs) are viable candidates for blue-light-emitting diodes compared to mixed-halide CsPb(Br/Cl)3 nanocrystals that exhibit segregation of halide ions. Unfortunately, the NPLs suffer from poor color stability and insufficient colloidal stability. However, the improvement in their stability has not been extensively studied. In this study, the blue emission of CsPbBr3 NPLs is stabilized by B-site doping approach, wherein a few of Pb2+ octahedral sites are replaced with small-sized Cu2+ ions. Cu2+-doped NPLs exhibit greater longterm stability in ambient conditions and better photostability compared to their undoped counterparts. First-principles calculations reveal that the Jahn–Teller distortion of (CuBr6)4− octahedra results in the shorter Pb-Br bonds and contraction of the entire CsPbBr3 lattice, which in turn increases the band gap of the CsPbBr3 NPLs. Cu2+ dopants also reduce the surface energy of the NPLs and impart highly desirable long-term stability. Therefore, the outcomes of this work might be a step forward towards improving the stability of two-dimensional perovskite NPLs.
Ultrafast ion transport at a cathode-electrolyte interface and its strong dependence on salt solvation
Bohua Wen, Zhi Deng, Ping-Chun Tsai, Zachary W. Lebens-Higgins, Louis F J Piper, Shyue Ping Ong, Yet-Ming Chiang
Nature Energy, 2020, 5, 578–586
Abstract
To access the full performance potential of advanced batteries, electrodes and electrolytes must be designed to facilitate ion transport at all applicable length scales. Here, we perform electrodynamic measurements on single electrode particles of ~6 nAh capacity, decouple bulk and interfacial transport from other pathways and show that Li intercalation into LiNi0.33 Mn0.33 Co0.33 O2 (NMC333) is primarily impeded by interfacial kinetics when using a conventional LiPF 6 salt. Electrolytes containing LiTFSI salt, with or without LiPF6 , exhibit about 100-fold higher exchange current density under otherwise identical conditions. This anion group effect is explained using molecular dynamics simulations to identify preferred solvation structures, density functional theory calculations of their binding energies and Raman spectroscopy confirmation of solvation structure. We show that TFSI preferentially solvates Li + compared to PF6 − , and yet its preferred solvation structures provide a lower Li + binding energy, suggesting a lower desolvation energy consistent with ultrafast interfacial kinetics.
Genetic algorithm-guided deep learning of grain boundary diagrams: Addressing the challenge of five degrees of freedom
Chongze Hu, Yunxing Zuo, Chi Chen, Shyue Ping Ong, Jian Luo
Materials Today, 2020, 38, 49-57
Abstract
Grain boundaries (GBs) often control the processing and properties of polycrystalline materials. Here, potentially transformative research is represented by constructing GB property diagrams as functions of temperature and bulk composition, also called “complexion diagrams,” as a general materials science tool on par with phase diagrams. However, a GB has five macroscopic (crystallographic) degrees of freedom (DOFs). It is essentially a “mission impossible” to construct property diagrams for GBs as a function of five DOFs by either experiments or modeling. Herein, we combine isobaric semi-grand canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN) models to tackle this grand challenge. The DNN prediction is ~10^8 faster than atomistic simulations, thereby enabling the construction of the property diagrams for millions of distinctly different GBs of five DOFs. Notably, excellent prediction accuracies have been achieved for not only symmetric-tilt and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter are more complex and much less understood, but they are ubiquitous and often limit the performance properties of real polycrystals as the weak links. The data-driven prediction of GB properties as function of temperature, bulk composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new paradigm.
Revealing Nanoscale Solid-Solid Interfacial Phenomena for Long-Life and High-Energy All-Solid-State Batteries
Abhik Banerjee, Hanmei Tang, Xuefeng Wang, Ju-Hsiang Cheng, Han Nguyen, Minghao Zhang, Darren H. S. Tan, Thomas A. Wynn, Erik A. Wu, Jean-Marie Doux, Tianpin Wu, Lu Ma, George E. Sterbinsky, Macwin Savio D’Souza, Shyue Ping Ong, Ying Shirley Meng
ACS Applied Materials & Interfaces, 2019, 11, 46, 43138-43145
Abstract
Enabling long cyclability of high-voltage oxide cathodes is a persistent challenge for all-solid-state batteries, largely because of their poor interfacial stabilities against sulfide solid electrolytes. While protective oxide coating layers such as LiNbO3 (LNO) have been proposed, its precise working mechanisms are still not fully understood. Existing literature attributes reductions in interfacial impedance growth to the coating’s ability to prevent interfacial reactions. However, its true nature is more complex, with cathode interfacial reactions and electrolyte electrochemical decomposition occurring simultaneously, making it difficult to decouple each effect. Herein, we utilized various advanced characterization tools and first-principles calculations to probe the interfacial phenomenon between solid electrolyte Li6PS5Cl (LPSCl) and high-voltage cathode LiNi0.85Co0.1Al0.05O2 (NCA). We segregated the effects of spontaneous reaction between LPSCl and NCA at the interface and quantified the intrinsic electrochemical decomposition of LPSCl during cell cycling. Both experimental and computational results demonstrated improved thermodynamic stability between NCA and LPSCl after incorporation of the LNO coating. Additionally, we revealed the in situ passivation effect of LPSCl electrochemical decomposition. When combined, both these phenomena occurring at the first charge cycle result in a stabilized interface, enabling long cyclability of all-solid-state batteries.
Chlorine-Doped Perovskite Oxide: A Platinum-Free Cathode for Dye-Sensitized Solar Cells
Wei Wang, Richard Tran, Jifa Qu, Yu Liu, Chi Chen, Meigui Xu, Yubo Chen, Shyue Ping Ong, Lianzhou Wang, Wei Zhou, Zongping Shao
ACS Applied Materials & Interfaces, 2019, 11, 39, 35641-35652
Abstract
Triiodide/iodide (I3−/I−) redox couple-mediated solar cells, batteries, and electrochromic devices require highly efficient and stable electrocatalysts for I3− reduction reaction (IRR) to overcome performance limitations, whereas the widely used platinum (Pt) cathode for IRR has limitations of high price and unfavorable durability. In this work, we present a halogen element (chlorine) doping strategy to design low-cost perovskite-type electrocatalysts with enhanced IRR activity and stability. The dye-sensitized solar cell (DSSC) assembled by the LaFeO2.965−δCl0.035 cathode delivers an attractive power conversion efficiency (PCE) of 11.4% with a remarkable PCE enhancement factor of 23% compared with Pt, which is higher than most of the reported non-Pt DSSC cathodes.
Engineering of K3YSi2O7 To Tune Photoluminescence with Selected Activators and Site Occupancy
Jianwei Qiao, Mahdi Amachraa, Maxim Molokeev, Yu-Chun Chuang, Shyue Ping Ong, Qinyuan Zhang, Zhiguo Xia
Chemistry of Materials, 2019, 31, 18, 7770-7778
Abstract
The luminescence of rare earth ions (Eu2+, Ce3+, and Eu3+)-doped inorganic solids is attractive for the screening of phosphors applied in solid-state lighting and displays and significant to probe the occupied crystallographic sites in the lattice also offering new routes to photoluminescence tuning. Here, we report on the discovery of the Eu- and Ce-activated K3YSi2O7 phosphors. K3YSi2O7:Eu is effectively excited by 450 nm InGaN blue light-emitting diodes (LEDs) and displays an orange-red emission originated from characteristic transitions of both Eu2+ and Eu3+, while K3YSi2O7:Ce3+ shows green emission upon 394 nm nearultraviolet (NUV) light excitation. Rietveld refinement verifies the successful doping of the activators, and density functional theory (DFT) calculations further support that Eu2+ occupies both K1 and Y2 crystallographic sites, while Ce3+ and Eu3+ only occupy the Y2 site; hence, the broad-band red emission of Eu2+ are attributed to a small DFT band gap (3.69 eV) of K3YSi2O7 host and a selective occupancy of Eu2+ in a highly distorted K1 site and a high crystal field splitting around Y2 sites. The white LEDs device utilizing orange-red-emitting K3YSi2O7:Eu and green-emitting K3YSi2O7:Ce3+ exhibits an excellent CRI of 90.1 at a correlated color temperature of 4523 K. Our work aims at bridging multivalent Eu2+/Eu3+ and Ce3+ site occupancy in the same host to realize photoluminescence tuning and especially exposes new ways to explore new phosphors with multicolor emission pumped by blue and NUV light for white LEDs.
Enabling Thin and Flexible Solid-State Composite Electrolytes by the Scalable Solution Process
Darren H. S. Tan, Abhik Banerjee, Zhi Deng, Erik A. Wu, Han Nguyen, Jean-Marie Doux, Xuefeng Wang, Ju-hsiang Cheng, Shyue Ping Ong, Ying Shirley Meng, Zheng Chen
ACS Applied Energy Materials, 2019, 2, 9, 6542–6550
Abstract
All solid-state batteries (ASSBs) have the potential to deliver higher energy densities, wider operating temperature range, and improved safety compared with today’s liquid-electrolyte-based batteries. However, of the various solid-state electrolyte (SSE) classespolymers, sulfides, or oxidesnone alone can deliver the combined properties of ionic conductivity, mechanical, and chemical stability needed to address scalability and commercialization challenges. While promising strategies to overcome these include the use of polymer/oxide or sulfide composites, there is still a lack of fundamental understanding between different SSE−polymer−solvent systems and its selection criteria. Here, we isolate various SSE−polymer−solvent systems and study their molecular level interactions by combining various characterization tools. With these findings, we introduce a suitable Li7P3S11SSE−SEBS polymer−xylene solvent combination that significantly reduces SSE thickness (∼50 μm). The SSE−polymer composite displays high room temperature conductivity (0.7 mS cm−1) and good stability with lithium metal by plating and stripping over 2000 h at 1.1 mAh cm−2. This study suggests the importance of understanding fundamental SSE−polymer−solvent interactions and provides a design strategy for scalable production of ASSBs.
Data-Driven Discovery of Full-Visible-Spectrum Phosphor
Shuxing Li, Yonghui Xia, Mahdi Amachraa, Nguyen Tuan Hung, Zhenbin Wang, Shyue Ping Ong, Rong-Jun Xie
Chemistry of Materials, 2019, 31, 16, 6286-6294
Abstract
The development of extra-broadband phosphors is essential for next-generation illumination with better color experience. In this work, we report the discovery of the first-known Eu2+-activated full-visible-spectrum phosphor, Sr2AlSi2O6N:Eu2+, identified by combining data mining of high-throughput density functional theory calculations and experimental characterization. Excited by UV-light-emitting diodes (LEDs), Sr2AlSi2O6N:Eu2+ shows a superbroad emission with a bandwidth of 230 nm, the broadest emission bandwidth ever reported, and has excellent thermal quenching resistance (88% intensity at 150 °C). A prototype white LED utilizing only this full-visible-spectrum phosphor exhibits superior color quality (Ra = 97, R9 = 91), outperforming commercial tricolor phosphor-converted LEDs. These findings not only show great promise of Sr2AlSi2O6N:Eu2+ as a single white emitter but also open up in silico design of full-visible-spectra phosphor in a single-phase material to address the reabsorption energy loss in commercial tricolor phosphor mixture.
Water Contributes to Higher Energy Density and Cycling Stability of Prussian Blue Analogue Cathodes for Aqueous Sodium-Ion Batteries
Xingyu Guo, Zhenbin Wang, Zhi Deng, Xiangguo Li, Bo Wang, Xi Chen, Shyue Ping Ong
Chemistry of Materials, 2019, 31, 15, 5933-5942
Abstract
In this work, we performed a comprehensive study of Prussian blue and its analogues (PBAs), one of the most promising cathode materials for aqueous sodium-ion batteries for large-scale energy-storage systems, using firstprinciples calculations. It is confirmed that dry PBAs generally undergo a phase transition from a rhombohedral Na2PR(CN)6 (where P and R are transition metals) to a tetragonal/cubic PR(CN)6 during Na extraction, in agreement with experimental observations. Using a grand potential phase diagram construction, we show that water and Na co-intercalation result in fundamentally different phase transition behavior and, hence, electrochemical voltage profiles in wet versus dry electrolytes. Lattice water increases the average voltage and reduces the volume change during electrochemical cycling, resulting in both higher energy density and better cycling stability. Finally, we identified four new PBA compositions, Na2CoMn(CN)6, Na2NiMn(CN)6, Na2CuMn(CN)6, and Na2ZnMn(CN)6, that show great promise as cathodes for aqueous rechargeable Na-ion batteries.
Color tunable single-phase Eu2+ and Ce3+ co-activated Sr2LiAlO4 phosphors
Jungmin Ha, Yoon Hwa Kim, Ekaterina Novitskaya, Zhenbin Wang, Maritza Sanchez, Olivia A. Graeve, Shyue Ping Ong, Won Bin Im, Joanna McKittrick
Journal of Materials Chemistry C, 2019, 7, 7734-7744
Abstract
High purity Eu2+ and Ce3+ singly and co-activated Sr2LiAlO4 phosphors were successfully synthesized through a facile combustion reaction. Fabrication of color tunable, single-phase phosphors was achieved by varying the Eu2+ /Ce3+ ratio that utilized the energy transfer between Ce3+ to Eu2+. For the singly activated compositions, the highest quantum efficiencies were 25% and 40% for Sr1.998Eu0.002LiAlO4 and Sr1.998Ce0.002LiAlO4 respectively. The emission of Sr2LiAlO4:Ce3+ and the excitation of Sr2LiAlO4:Eu2+ overlap in the range of 400 nm - 500 nm so that energy transfer from Ce3+ → Eu2+ takes place. The emission color of Eu2+ and Ce3+ co-activated Sr2LiAlO4 changes from blue, to cool-white, to green depending on the activators concentrations. The maximum quantum efficiency of Eu2+ and Ce3+ co-activated Sr2LiAlO4 was 55% when the concentrations of Eu2+ and Ce3+ were 0.005 and 0.001, respectively, which demonstrates that the corresponding quantum efficiency improves by co-activating with Eu2+ and Ce3+.
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, Shyue Ping Ong
Chemistry of Materials, 2019, 31, 9, 3564-3572
Abstract
Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals. We demonstrate that the MEGNet models outperform prior ML models such as the SchNet in 11 out of 13 properties of the QM9 molecule data set. Similarly, we show that MEGNet models trained on ∼60 000 crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps, and elastic moduli of crystals, achieving better than density functional theory accuracy over a much larger data set. We present two new strategies to address data limitations common in materials science and chemistry. First, we demonstrate a physically intuitive approach to unify four separate molecular MEGNet models for the internal energy at 0 K and room temperature, enthalpy, and Gibbs free energy into a single free energy MEGNet model by incorporating the temperature, pressure, and entropy as global state inputs. Second, we show that the learned element embeddings in MEGNet models encode periodic chemical trends and can be transfer-learned from a property model trained on a larger data set (formation energies) to improve property models with smaller amounts of data (band gaps and elastic moduli).
Artificial intelligence is aiding the search for energy materials
Prachi Patel, Shyue Ping Ong
MRS Bulletin, 2019, 44, 3, 162-163
Abstract
The combination of big data and AI is being called the “fourth industrial revolution,” and its applications in materials science have soared in the past decade. The AI subfield of machine learning is already aiding the discovery of new materials.
Studies of Functional Defects for Fast Na-Ion Conduction in Na3−yPS4−xClx with a Combined Experimental and Computational Approach
Xuyong Feng, Po-Hsiu Chien, Zhuoying Zhu, Iek-Heng Chu, Pengbo Wang, Marcello Immediato-Scuotto, Hesam Arabzadeh, Shyue Ping Ong, Yan-Yan Hu
Advanced Functional Materials, 2019, 1807951, 1807951
Abstract
All-solid-state rechargeable sodium (Na)-ion batteries are promising for inexpensive and high-energy-density large-scale energy storage. In this contribution, new Na solid electrolytes, Na3−yPS4−xClx, are synthesized with a strategic approach, which allows maximum substitution of Cl for S (x = 0.2) without significant compromise of structural integrity or Na deficiency. A maximum conductivity of 1.96 mS cm−1 at 25°C is achieved for Na3.0PS3.8Cl0.2 , which is two orders of magnitude higher compared with that of tetragonal Na3PS4 (t-Na3PS4). The activation energy (Ea) is determined to be 0.19 eV. Ab initio molecular dynamics simulations shed light on the merit of maximizing Cldoping while maintaining low Na deficiency in enhanced Na-ion conduction. Solid-state nuclear magnetic resonance (NMR) characterizations confirm the successful substitution of Cl for S and the resulting change of P oxidation state from 5+ to 4+, which is also verified by spin moment analysis. Ion transport pathways are determined with a tracer-exchange NMR method. The functional detects that promote Na -ion transport are maximized for further improvement in ionic conductivity. Full-cell performance is demonstrated using Na/Na3.0PS3.8Cl0.2/Na3V2(PO4)3 with a reversible capacity of ≈100 mAh g-1 at room temperature.
Rational Synthesis and Electrochemical Performance of LiVOPO4 Polymorphs
Marc F. V. Hidalgo, Yuh-Chieh Lin, Antonin Grenier, Dongdong Xiao, Jatinkumar Rana, Huolin Xin, Richard Tran, Mateusz J Zuba, Jennifer Donohue, Frederick O. Omenya, Iek-Heng Chu, Zhenbin Wang, XiangGuo Li, Natasha Chernova, Karena W. Chapman, Guangwen Zhou, Louis F.J. Piper, Shyue Ping Ong, M. Stanley Whittingham
Journal of Materials Chemistry A, 2019
Abstract
LiVOPO4 is a promising cathode material for Li-ion batteries due to its ability to intercalate up to two electrons per vanadium redox center. However, LiVOPO4 exhibits polymorphism, forming either the α I , β, or ϵ phase. A thorough comparison between the properties of these phases is difficult because they usually differ in synthesis methods. In this study, we synthesize all three polymorphs by annealing a single precursor, LiVOPO4.2H2O, thereby reducing the effect of synthesis on the properties of the materials. We show through in-situ XRD with heating and DFT calculations that, in terms of stability, α I -LiVOPO4 \textless ϵ-LiVOPO4 ≤ β-LiVOPO4 . We also show experimentally and through DFT calculations that the tolerance to Ointerstitials and O-vacancies can explain the differences in stability, morphology, and electrochemical performance between β- and ϵ-LiVOPO4 . β-LiVOPO4 is more stable in the presence of O-interstitials while ϵ-LiVOPO4 is favored in the presence of O-vacancies. These defects affect the surface energies and morphology of the products formed, which are confirmed in the Wulff shape calculations and Transmission Electron Microscopy images. These imply that β-LiVOPO4 has an improved rate performance under an oxidizing atmosphere due to the increased presence of facets with superior ion diffusion at the surface. This improved performance is seen by the improved rate capability and capacity of β-LiVOPO4 in the high-voltage region. In contrast, synthesis conditions have little effect on improving the rate performance of ϵ-LiVOPO4 .
An electrostatic spectral neighbor analysis potential for lithium nitride
Zhi Deng, Chi Chen, Xiang-Guo Li, Shyue Ping Ong
npj Computational Materials, 2019, 5, 1, 75
Abstract
Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy. However, a challenge in their application to ionic systems is the treatment of long-ranged electrostatics. Here, we present a highly accurate electrostatic Spectral Neighbor Analysis Potential (eSNAP) for ionic α-Li3N, a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion batteries. We show that the optimized eSNAP model substantially outperforms traditional Coulomb-Buckingham potential in the prediction of energies and forces, as well as various properties, such as lattice constants, elastic constants, and phonon dispersion curves. We also demonstrate the application of eSNAP in long-time, large-scale Li diffusion studies in Li3N, providing atomistic insights into measures of concerted ionic motion (e.g., the Haven ratio) and grain boundary diffusion. This work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism, enabling large-scale atomistic simulations for such systems.
Anisotropic work function of elemental crystals
Richard Tran, Xiang-Guo Li, Joseph H. Montoya, Donald Winston, Kristin Aslaug Persson, Shyue Ping Ong
Surface Science, 2019, 687, 48-55
Abstract
The work function is a fundamental electronic property of a solid that varies with the facets of a crystalline surface. It is a crucial parameter in spectroscopy as well as materials design, especially for technologies such as thermionic electron guns and Schottky barriers. In this work, we present the largest database of calculated work functions for elemental crystals to date. This database contains the anisotropic work functions of more than 100 polymorphs of about 72 elements and up to a maximum Miller index of two and three for non-cubic and cubic crystals, respectively. The database has been rigorously validated against previous experimental and computational data where available. We also propose a weighted work function based on the Wulff shape that can be compared to measurements from polycrystalline specimens, and show that this weighted work function can be modeled empirically using simple atomic parameters. Furthermore, for the first time, we were able to analyze simple bond breaking rules for metallic systems beyond a maximum Miller index of one, allowing for a more generalized investigation of work function anisotropy.
Elucidating the Limit of Li Insertion into the Spinel Li4Ti5O12
Haodong Liu, Zhuoying Zhu, Jason Huang, Xin He, Yan Chen, Rui Zhang, Ruoqian Lin, Yejing Li, Sicen Yu, Xing Xing, Qizhang Yan, Xiangguo Li, Matthew J. Frost, Ke An, Jun Feng, Robert Kostecki, Huolin Xin, Shyue Ping Ong, Ping Liu
ACS Materials Letters, 2019, 1, 1, 96-102
Abstract
In this work, we show that the well-known lithium-ion anode material, Li4Ti5O12 , exhibits exceptionally high initial capacity of 310 mAh g−1 when it is discharged to 0.01 V. It maintains a reversible capacity of 230 mAh g−1 , far exceeding the “theoretical” capacity of 175 mAh g−1 when this anode is lithiated to the composition Li7Ti5O12 . Neutron diffraction analyses identify that additional Li reversibly enters into the Li7Ti5O12 to form Li8Ti5O12. Density functional theory (DFT) calculations reveal the average potentials of the Li4Ti5O12 to Li7Ti5O12 step and the Li7Ti5O12 to Li8Ti5O12 step are 1.57 and 0.19 V, respectively, which are in excellent agreement with experimental results. Transmission electron microscopy (TEM) studies confirm that the irreversible capacity of Li4Ti5O12 during its first cycle originates from the formation of a solid electrolyte interface (SEI) layer. This work clarifies the fundamental lithiation mechanism of the Li4Ti5O12 , when lithiated to 0.01 V vs Li.
2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches
Jun Zhou, Lei Shen, Miguel Dias Costa, Kristin A. Persson, Shyue Ping Ong, Patrick Huck, Yunhao Lu, Xiaoyang Ma, Yiming Chen, Hanmei Tang, Yuan Ping Feng
Scientific Data, 2019, 6, 1, 86
Abstract
Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered structures by a topology-based algorithm and theoretically exfoliated them into monolayers. Then, we generated new 2D materials by chemical substitution of elements in known 2D materials by others from the same group in the periodic table. The structural, electronic and energetic properties of these 2D materials are consistently calculated, to provide a starting point for further material screening, data mining, data analysis and artificial intelligence applications. We present the details of computational methodology, data record and technical validation of our publicly available data (http:// www.2dmatpedia.org/).
Accelerating materials science with high-throughput computations and machine learning
Shyue Ping Ong
Computational Materials Science, 2019, 161, October 2018, 143–150
Abstract
With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. Here, we review our efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.
Automated generation and ensemble-learned matching of X-ray absorption spectra
Chen Zheng, Kiran Mathew, Chi Chen, Yiming Chen, Hanmei Tang, Alan Dozier, Joshua J. Kas, Fernando D. Vila, John J. Rehr, Louis F. J. Piper, Kristin A. Persson, Shyue Ping Ong
npj Computational Materials, 2018, 4, 1, 12
Abstract
X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green's function approach to calculate X-ray absorption spectra. We will demonstrate that the ELSIE algorithm, which combines 33 weak “learners” comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.
Role of Zr in strengthening MoSi2 from density functional theory calculations
Hui Zheng, Richard Tran, Xiang-Guo Li, Balachandran Radhakrishnan, Shyue Ping Ong
Acta Materialia, 2018, 145, 470–476
Abstract
MoSi2 is an important intermetallic with excellent oxidation resistance at high temperatures above 1000 °C. However, its application at lower temperatures is limited by oxygen embrittlement, a phenomenon known as “pesting”. In this work, we comprehensively investigate the role of Zr in mitigating pesting in MoSi2 using density functional theory calculations. We show that Zr dopants reduce the embrittling effects of oxygen interstitials at MoSi2 grain boundaries by being a charge donor to oxygen. However, a more substantial effect is observed when Zr is present as a secondary getter nanoparticle phase. Oxygen interstitials have a strong thermodynamic driving force to migrate the Zr subsurface at the Zr/MoSi2 interface, and the work of separation of the clean and oxygen-contaminated Zr/MoSi2 interfaces are much higher than that of MoSi2 grain boundaries. Finally, we present an efficient screening approach to identify other potential getter elements using simple thermodynamic descriptors, which can be extended to other alloy systems of interest. These findings provide crucial fundamental insights and further avenues to optimize Mo and other alloys.
Deep neural networks for accurate predictions of crystal stability
Weike Ye, Chi Chen, Zhenbin Wang, Iek-Heng Chu, Shyue Ping Ong
Nature Communications, 2018, 9, 1, 3800
Abstract
Predicting the stability of crystals is one of the central problems in materials science. Today, density functional theory (DFT) calculations remain comparatively expensive and scale poorly with system size. Here we show that deep neural networks utilizing just two descriptors—the Pauling electronegativity and ionic radii—can predict the DFT formation energies of C 3 A 2 D 3 O 12 garnets and ABO 3 perovskites with low mean absolute errors (MAEs) of 7–10 meV atom −1 and 20–34 meV atom −1 , respectively, well within the limits of DFT accuracy. Further extension to mixed garnets and perovskites with little loss in accuracy can be achieved using a binary encoding scheme, addressing a critical gap in the extension of machine-learning models from fixed stoichiometry crystals to infinite universe of mixedspecies crystals. Finally, we demonstrate the potential of these models to rapidly transverse vast chemical spaces to accurately identify stable compositions, accelerating the discovery of novel materials with potentially superior properties.
Predictive modeling and design rules for solid electrolytes
Gerbrand Ceder, Shyue Ping Ong, Yan Wang
MRS Bulletin, 2018, 43, 10, 746–751
Abstract
All-solid-state batteries utilizing a ceramic instead of an organic liquid as an electrolyte have the potential to be safer and more energy dense than traditional rechargeable lithium-ion batteries. This emergent energy-storage technology, however, is still critically limited by the performance of the solid electrolyte and its interface with electrodes. Here, we present a review of recent efforts in predictive modeling and materials design for lithium and sodium solid electrolytes using advanced computational approaches. These approaches have enabled the efficient design and discovery of new functional materials with desired properties, such as high alkali ionic conductivity, good phase and electrochemical stability, and low cost, accelerating the development of all-solid-state alkali batteries.
Harnessing the Materials Project for machine-learning and accelerated discovery
Weike Ye, Chi Chen, Shyam Dwaraknath, Anubhav Jain, Shyue Ping Ong, Kristin A. Persson
MRS Bulletin, 2018, 43, 09, 664–669
Abstract
Improvements in computational resources over the last decade are enabling a new era of computational prediction and design of novel materials. The resulting resources are databases such as the Materials Project ( www.materialsproject.org ), which is harnessing the power of supercomputing together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials, to design novel materials, and to make the data available for free to the community, together with online analysis and design algorithms. The current release contains data derived from quantum mechanical calculations for more than 70,000 materials and millions of associated materials properties. The software infrastructure carries out thousands of calculations per week, enabling screening and predictions for both novel solids as well as molecular species with targeted properties. As the rapid growth of accessible computed materials properties continues, the next frontier is harnessing that information for automated learning and accelerated discovery. In this article, we highlight some of the emerging and exciting efforts, and successes, as well as current challenges using descriptor-based and machine-learning methods for data-accelerated materials design.
Structural Changes in a High-Energy Density VO2F Cathode upon Heating and Li Cycling
Xiaoya Wang, Yuh-Chieh Lin, Hui Zhou, Fredrick Omenya, Iek-Heng Chu, Khim Karki, Shawn Sallis, Jatinkumar Rana, Louis F. J. Piper, Natasha A. Chernova, Shyue Ping Ong, M. Stanley Whittingham
ACS Applied Energy Materials, 2018, acsaem.8b00473
Abstract
Structural changes in VO2F, which allow twoelectron transfer during electrochemical Li cycling, were investigated. This compound adopts a rhombohedral structure, space group R-3c, with O and F sharing one site, and was synthesized by high-energy ball-milling. The thermal stability of VO2F, which is related to the battery safety, is studied by in situ XRD upon heating and by thermal gravimetric analysis. VO2F is found to be stable up to 160 °C under inert atmosphere; above this temperature, it converts into vanadium oxide with fluorine loss. The structure evolution upon lithium cycling was studied by ex situ X-ray diffraction and absorption techniques. The results show that lithiation of VO2F goes through a solid-solution reaction, and the rhombohedral structure is preserved if no more than one lithium ion is intercalated. Upon a second Li insertion, an irreversible transition to a rock-salt structure occurs. We show using first-principles calculations that this irreversible transformation can be explained by an asymmetric energetic preference between the rhombohedral and rock-salt forms of LixVO2F, which result in large thermodynamic driving forces to convert to the rock-salt structure at x \textgreater 1 and relatively small thermodynamic driving forces to convert back to the rhombohedral structure when delithiating to x \textless 1.
Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals
Xiang-Guo Li, Chongze Hu, Chi Chen, Zhi Deng, Jian Luo, Shyue Ping Ong
Physical Review B, 2018, 98, 9, 094104
Abstract
In recent years, efficient inter-atomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, to translation, rotation, permutation of homonuclear atoms, among others. However, most such potential development efforts have largely focused on simple elemental systems. In this work, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over well-established, high-performing embedded atom method (EAM) and modified EAM (MEAM) potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully-constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. This work provides a systematic model development process for multi-component systems, including an efficient procedure to optimize the hyper-parameters in the model fitting, and paves the way to long-time, large-scale simulations of non-elemental systems.
High-throughput computational X-ray absorption spectroscopy
Kiran Mathew, Chen Zheng, Donald Winston, Chi Chen, Alan Dozier, John J Rehr, Shyue Ping Ong, Kristin A Persson
Scientific Data, 2018, 5, 180151
Abstract
X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique. In this work we present a database of computed XAS spectra, using the Green's formulation of the multiple scattering theory implemented in the FEFF code. With more than 500,000 K-edge X-ray absorption near edge (XANES) spectra for more than 40,000 unique materials, this database constitutes the largest existing collection of computed XAS spectra to date. The data is openly distributed via the Materials Project, enabling researchers across the world to access it for free and use it for comparisons with experiments and further analysis.
KVOPO4 : A New High Capacity Multielectron Na-Ion Battery Cathode
Jia Ding, Yuh-chieh Lin, Jue Liu, Jatinkumar Rana, Hanlei Zhang, Hui Zhou, Iek-heng Chu, Kamila M Wiaderek, Fredrick Omenya, Natasha A Chernova, Karena W Chapman, Louis F J Piper, Shyue Ping Ong, M Stanley Whittingham
Advanced Energy Materials, 2018, 1800221, 1800221
Abstract
Sodium ion batteries have attracted much attention in recent years, due to the higher abundance and lower cost of sodium, as an alternative to lithium ion batteries. However, a major challenge is their lower energy density. In this work, we report a novel multi-electron cathode material, KVOPO4 , for sodium ion batteries. Due to the unique polyhedral framework, the V3+ ↔ V4+ ↔ V5+ redox couple was for the first time fully activated by sodium ions in a vanadyl phosphate phase. The KVOPO4 based cathode delivered reversible multiple sodium (i.e. maximum 1.66 Na + per formula unit) storage capability, which leads to a high specific capacity of 235 Ah kg−1 . Combining an average voltage of 2.56 V vs. Na/Na + , a high practical energy density of over 600 Wh kg−1 was achieved, the highest yet reported for any sodium cathode material. The cathode exhibits a very small volume change upon cycling (1.4% for 0.64 sodium and 8.0% for 1.66 sodium ions). Density functional theory (DFT) calculations indicate that the KVOPO4 framework is a 3D ionic conductor with a reasonably, low Na+ migration energy barrier of ≈450 meV, in line with the good rate capability obtained.
Mining Unexplored Chemistries for Phosphors for High-Color-Quality White-Light-Emitting Diodes
Zhenbin Wang, Jungmin Ha, Yoon Hwa Kim, Won Bin Im, Joanna McKittrick, Shyue Ping Ong
Joule, 2018, 2, 5, 914–926
Abstract
There is a critical need for new earth-abundant phosphors to enable next-generation, highly efficient solid-state lighting. Here, we report the discovery of Sr2LiAlO4, the first known Sr-Li-Al-O quaternary crystal, via a carefully-targeted data-driven structure prediction and screening effort using density functional theory calculations. Sr2LiAlO4 is predicted and experimentally confirmed to be a thermodynamically and thermally stable phosphor host that can be excited with near-UV/blue sources. The Eu2+ and Ce3+-activated Sr2LiAlO4 phosphors exhibit broad emissions at max ∼ 512 nm (green-yellow) and max ∼ 434 nm (blue), respectively, with excellent thermal quenching resistance of \textgreater 88% intensity at 150 C. A prototype phosphor-converted white LED utilizing Sr2LiAlO4-based phosphors yields an excellent color rendering index exceeding 90. Sr2LiAlO4 therefore exhibits great potential for industrial applications in low-cost, high-color-quality WLEDs.
Understanding the Electrochemical Properties of Naphthalene Diimide: Implication for Stable and High-Rate Lithium-Ion Battery Electrodes
Yang Shi, Hanmei Tang, Shengli Jiang, Laure V. Kayser, Mingqian Li, Fang Liu, Fei Ji, Darren J. Lipomi, Shyue Ping Ong, Zheng Chen
Chemistry of Materials, 2018, 30, 10, 3508–3517
Abstract
Redox-active organic molecules have attracted much attention as alternatives to transition metal-based electrodes for lithium-ion batteries due to their low cost and large abundance. However, the relatively low cycling stability still prevents some of the most promising molecules to be used as lithium-ion electrodes. Herein, we used 1,4,5,8-naphthalenediimide (NDI) as a model molecule to systematically investigate its intrinsic electrochemical property, including its electrolyte compatibility, maximum capacity, cycling stability and rate capability in different organic electrolytes. Extensive physicochemical characterization, electrochemical measurement and density function theory (DFT) calculation together show that the electrode-electrolyte interaction is the key factor determining its specific capacity and cycling stability. With a proper selection of electrolytes, NDI molecule, which was considered to be not stable for lithium storage, can achieve near theoretical capacity (based on 2-electron reaction), very high rate capability and high cycling stability. This study suggests the importance of understanding the fundamental electrode-electrolyte interactions in designing high-performance organic electrodes.
The Promise and Challenges of Quantum Computing for Energy Storage
Alan Ho, Jarrod McClean, Shyue Ping Ong
Joule, 2018, 2, 5, 810–813
Abstract
With recent advances by industry, the emergence of quantum computing at a capability that surpasses the limits of classical computing is fast approaching. An example of these advancements is the superconducting qubit technology developed at Google (as seen in Figure 1). A key area where quantum computing has been predicted to offer dramatic advances is in applications to materials science and quantum chemistry. Here there is a close link between the natural system and engineering quantum devices, allowing for dramatic advances in what can be simulated and how these systems can be understood. This subfield has evolved rapidly over the past few years with experimental demonstrations and theoretical advances alike. A broad perspective on this relationship is laid out in a recent review article. Our natural instinct is to harness the newly found and unprecedented problem-solving capabilities of quantum computing and direct them toward the defining challenges of our time. In this Future Energy, we frame and explore the opportunity of applying quantum computing to energy storage. Here we focus on computational materials design of batteries as a specific example.
Understanding the Electrochemical Mechanisms Induced by Gradient Mg2+ Distribution of Na-Rich Na3+xV2-xMgx(PO4)3 /C for Sodium Ion Batteries
Hui Li, Hanmei Tang, Chuze Ma, Ying Bai, Judith Alvarado, Balachandran Radhakrishnan, Shyue Ping Ong, Feng Wua, Ying Shirley Meng, Chuan Wu
Chemistry of Materials, 2018, 30, 8, 2498–2505
Abstract
Metal-ion doping can improve the electrochemical performance of Na3V2(PO4)3. However, the reason for the enhanced electrochemical performance and the effects of cation doping on the structure of Na3V2(PO4)3 have yet been probed. Herein, Mg2+ is doped into Na3V2(PO4)3/C according to the first-principles calculation. The results indicate that Mg2+ prefers to reside in the V site and an extra electrochemical active Na+ is introduced to the Na3V2(PO4)3/C crystal to maintain the charge balance. The distribution of Mg2+ in the particle of Na3V2(PO4)3/C is further studied by electrochemical impedance spectroscopy. We find that the highest distribution of Mg2+ on the surface of the particles leads to facile surface electrochemical reactions for Mg2+ doped samples, especially at high rates.
Predicting the volumes of crystals
Iek-heng Chu, Sayan Roychowdhury, Daehui Han, Anubhav Jain, Shyue Ping Ong
Computational Materials Science, 2018, 146, 184–192
Abstract
New crystal structures are frequently derived by performing ionic substitutions on known crystal structures. These derived structures are then used in further experimental analysis, or as the initial guess for structural optimization in electronic structure calculations, both of which usually require a reasonable guess of the lattice parameters. In this work, we propose two lattice prediction schemes to improve the initial guess of a candidate crystal structure. The first scheme relies on a one-to-one mapping of species in the candidate crystal structure to a known crystal structure, while the second scheme relies on data-mined minimum atom pair distances to predict the crystal volume of the candidate crystal structure and does not require a reference structure. We demonstrate that the two schemes can effectively predict the volumes within mean absolute errors (MAE) as low as 3.8% and 8.2%. We also discuss the various factors that may impact the performance of the schemes. Implementations for both schemes are available in the open-source pymatgen software.
New Insights into the Interphase between the Na Metal Anode and Sulfide Solid-State Electrolytes: A Joint Experimental and Computational Study
Erik A. Wu, Christopher S. Kompella, Zhuoying Zhu, Jungwoo Z. Lee, Steven C. Lee, Iek-Heng Chu, Han Nguyen, Shyue Ping Ong, Abhik Banerjee, Ying Shirley Meng
ACS Applied Materials & Interfaces, 2018, 10, 12, 10076–10086
Abstract
In this work, we investigated the interface between the sodium anode and the sulfide-based solid electrolytes Na3SbS4 (NAS), Na3PS4 (NPS), and Cl-doped NPS (NPSC) in all-solid-state-batteries (ASSBs). Even though these electrolytes have demonstrated high ionic conductivities in the range of 1 mS cm–1 at ambient temperatures, sulfide sold-state electrolytes (SSEs) are known to be unstable with Na metal, though the exact reaction mechanism and kinetics of the reaction remain unclear. We demonstrate that the primary cause of capacity fade and cell failure is a chemical reaction spurred on by electrochemical cycling that takes place at the interface between the Na anode and the SSEs. To investigate the properties of the Na-solid electrolyte interphase (SSEI) and its effect on cell performance, the SSEI was predicted computationally to be composed of Na2S and Na3Sb for NAS and identified experimentally via X-ray photoelectron spectroscopy (XPS). These two compounds give the SSEI mixed ionic- and electronic-conducting properties, which promotes continued SSEI growth, which increases the cell impedance at the expense of cell performance and cycle life. The SSEI for NPS was similarly found to be comprised of Na2S and Na3P, but XPS analysis of Cl-doped NPS (NPSC) showed the presence of an additional compound at the SSEI, NaCl, which was found to mitigate the decomposition of NPS. The methodologies presented in this work can be used to predict and optimize the electrochemical behavior of an all-solid-state cell. Such joint computational and experimental efforts can inform strategies for engineering a stable electrolyte and SSEI to avoid such reactions. Through this work, we call for more emphasis on SSE compatibility with both anodes and cathodes, essential for improving the electrochemical properties, longevity, and practicality of Na-based ASSBs.
First-Order Interfacial Transformations with a Critical Point: Breaking the Symmetry at a Symmetric Tilt Grain Boundary
Shengfeng Yang, Naixie Zhou, Hui Zheng, Shyue Ping Ong, Jian Luo
Physical Review Letters, 2018, 120, 8, 085702
Abstract
First-order interfacial phaselike transformations that break the mirror symmetry of the symmetric ∑5 (210) tilt grain boundary (GB) are discovered by combining a modified genetic algorithm with hybrid Monte Carlo and molecular dynamics simulations. Density functional theory calculations confirm this prediction. This first-order coupled structural and adsorption transformation, which produces two variants of asymmetric bilayers, vanishes at an interfacial critical point. A GB complexion (phase) diagram is constructed via semigrand canonical ensemble atomistic simulations for the first time.
Probing Solid-Solid Interfacial Reactions in All-Solid-State Sodium-Ion Batteries with First-Principles Calculations
Hanmei Tang, Zhi Deng, Zhuonan Lin, Zhenbin Wang, Iek-Heng Chu, Chi Chen, Zhuoying Zhu, Chen Zheng, Shyue Ping Ong
Chemistry of Materials, 2018, 30, 1, 163-173
Abstract
We present an exposition of first-principles approaches to elucidating interfacial reactions in all-solid-state sodium-ion batteries. We will demonstrate how thermodynamic approximations based on assumptions of fast alkali diffusion and multispecies equilibrium can be used to effectively screen combinations of Na-ion electrodes, solid electrolytes, and buffer oxides for electrochemical and chemical compatibility. We find that exchange reactions, especially between simple oxides and thiophosphate groups to form PO43–, are the main cause of large driving forces for cathode/solid electrolyte interfacial reactions. A high reactivity with large volume changes is also predicted at the Na anode/solid electrolyte interface, while the Na2Ti3O7 anode is predicted to be much more stable against a broad range of solid electrolytes. We identify several promising binary oxides, Sc2O3, SiO2, TiO2, ZrO2, and HfO2, that are similarly or more chemically compatible with most electrodes and solid electrolytes than the commonly used Al2O3 is. Finally, we show that ab initio molecular dynamics simulations of the NaCoO2/Na3PS4 interface model predict that the formation of SO42–-containing compounds and Na3P is kinetically favored over the formation of PO43–-containing compounds, in contrast to the predictions of the thermodynamic models. This work provides useful insights into materials selection strategies for enabling stable electrode/solid electrolyte interfaces, a critical bottleneck in designing all-solid-state sodium-ion batteries, and outlines several testable predictions for future experimental validation.
Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows
Kiran Mathew, Joseph H Montoya, Alireza Faghaninia, Shyam Dwarakanath, Muratahan Aykol, Hanmei Tang, Iek-heng Chu, Tess Smidt, Brandon Bocklund, Matthew Horton, John Dagdelen, Brandon Wood, Zi-Kui Liu, Jeffrey Neaton, Shyue Ping Ong, Kristin Persson, Anubhav Jain
Computational Materials Science, 2017, 139, 140–152
Abstract
We introduce atomate, an open-source Python framework for computational materials science simulation, analysis, and design with an emphasis on automation and extensibility. Built on top of open source Python packages already in use by the materials community such as pymatgen, FireWorks, and custodian, atomate provides well-tested workflow templates to compute various materials properties such as electronic bandstructure, elastic properties, and piezoelectric, dielectric, and ferroelectric properties. Atomate also enables the computational characterization of materials by providing workflows that calculate X-ray absorption (XAS), Electron energy loss (EELS) and Raman spectra. One of the major features of atomate is that it provides both fully functional workflows as well as reusable components that enable one to compose complex materials science workflows that use a diverse set of computational tools. Additionally, atomate creates output databases that organize the results from individual calculations and contains a builder framework that creates summary reports for each computed material based on multiple simulations.
Direct Observation of Halide Migration and its Effect on the Photoluminescence of Methylammonium Lead Bromide Perovskite Single Crystals
Yanqi Luo, Parisa Khoram, Sarah Brittman, Zhuoying Zhu, Barry Lai, Shyue Ping Ong, Erik C. Garnett, David P. Fenning
Advanced Materials, 2017, 29, 43, 1703451
Abstract
Optoelectronic devices based on hybrid perovskites have demonstrated outstanding performance within a few years of intense study. However, commercialization of these devices requires barriers to their development to be overcome, such as their chemical instability under operating conditions. To investigate this instability and its consequences, the electric field applied to single crystals of methylammonium lead bromide (CH3NH3PbBr3) is varied, and changes are mapped in both their elemental composition and photoluminescence. Synchrotron-based nanoprobe X-ray fluorescence (nanoXRF) with 250 nm resolution reveals quasi-reversible field-assisted halide migration, with corresponding changes in photoluminescence. It is observed that higher local bromide concentration is correlated to superior optoelectronic performance in CH3NH3PbBr3. A lower limit on the electromigration rate is calculated from these experiments and the motion is interpreted as vacancy-mediated migration based on nudged elastic band density functional theory (DFT) simulations. The XRF mapping data provide direct evidence of field-assisted ionic migration in a model hybrid-perovskite thin single crystal, while the link with photoluminescence proves that the halide stoichiometry plays a key role in the optoelectronic properties of the perovskite.
Accurate force field for molybdenum by machine learning large materials data
Chi Chen, Zhi Deng, Richard Tran, Hanmei Tang, Iek-Heng Chu, Shyue Ping Ong
Physical Review Materials, 2017, 1, 4, 043603
Abstract
In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mo's importance as a structural metal, existing force fields for Mo based on the embedded atom and modified embedded atom methods still do not provide satisfactory accuracy on many properties. We will show that by fitting to the energies, forces and stress tensors of a large density functional theory (DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including energies, forces, stresses, elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. We will outline a systematic model development process, which includes a rigorous approach to structural selection based on principal component analysis, as well as a differential evolution algorithm for optimizing the hyperparameters in the model fitting so that both the model error and the property prediction error can be simultaneously lowered. We expect that this newly developed Mo SNAP model will find broad applications in large-scale, long-time scale simulations.
Effects of Transition-Metal Mixing on Na Ordering and Kinetics in Layered P2 Oxides
Chen Zheng, Balachandran Radhakrishnan, Iek-Heng Chu, Zhenbin Wang, Shyue Ping Ong
Physical Review Applied, 2017, 7, 6, 064003
Abstract
Layered P2 oxides are promising cathode materials for rechargeable sodium-ion batteries. In this work, we systematically investigate the effect of transition metal (TM) mixing on Na ordering and kinetics in the NaxCo1-yMnyO2 model system using density functional theory (DFT) calculations. The DFT predicted 0K stability diagrams indicate that Co-Mn mixing reduces the energetic differences between Na orderings, which may account for the reduction of the number of phase transformations observed during cycling of mixed TM P2 layered oxides compared to single TM. Using ab initio molecular dynamics simulations and nudged elastic band calculations, we show that the TM composition at the Na(1) (face-sharing) site has a strong influence on the Na site energies, which in turns impacts the kinetics of Na diffusion towards the end of charge. By employing a site percolation model, we establish theoretical upper and lower bounds for TM concentration based on their effect on Na(1) site energies, providing a framework to rationally tune mixed TM compositions for optimal Na diffusion.
Divalent-doped Na3Zr2Si2PO12 natrium superionic conductor: Improving the ionic conductivity via simultaneously optimizing the phase and chemistry of the primary and secondary phases
Mojtaba Samiee, Balachandran Radhakrishnan, Zane Rice, Zhi Deng, Ying Shirley Meng, Shyue Ping Ong, Jian Luo
Journal of Power Sources, 2017, 347, 229–237
Abstract
NASICON is one of the most promising sodium solid electrolytes that can enable the assembly of cheaper and safer sodium all-solid-state batteries. In this study, we perform a combined experimental and computational investigation into the effects of aliovalent doping in NASICON on both bulk and grain boundary (secondary phase) ionic conductivity. Our results show that the dopants with low solid solubility limits in NASICON lead to the formation of a conducting (less insulating) secondary phase, thereby improving the grain boundary conductivity measured by electrochemical impedance spectroscopy (including grain-boundary, secondary-phase, and other microstructural contributions) that is otherwise hin-dered by the poorly-conducting secondary phases in undoped NASICON. This is accompanied by a change in the Si/ P ratio in the primary NASICON bulk phase, thereby transforming monoclinic NASICON to rhombohedral NASICON. Consequently, we have synthesized NASICON chemistries with significantly improved and optimized total ionic con-ductivity of 2.7 mS/cm. More importantly, this study has achieved an understanding of the underlying mechanisms of improved conductivities via doping (differing from the common wisdom) and further suggests a new general direction to improve the ionic conductivity of solid electrolytes via simultaneously optimizing the primary bulk phase and the mi-crostructure (including grain boundary segregation and secondary phases).
Li3Y(PS4)2 and Li5PS4Cl2 : New Lithium Superionic Conductors Predicted from Silver Thiophosphates using Efficiently Tiered Ab Initio Molecular Dynamics Simulations
Zhuoying Zhu, Iek-Heng Chu, Shyue Ping Ong
Chemistry of Materials, 2017, 29, 6, 2474–2484
Abstract
We report two novel, earth-abundant lithium superionic conductors, Li3Y(PS4)2 and Li5PS4Cl2, that are predicted to satisfy the necessary combination of good phase stability, high Li+ conductivity, wide band gap and good electrochemical stability for solid electrolyte applications in all-solid-state rechargeable lithium-ion batteries. These candidates were identified from a high-throughput first principles screening of the Li-P- S ternary and Li-M-P-S (where M is a non-redox-active element) quaternary chemical spaces, including candidates obtained by replacing Ag with Li in the Ag-P-S and Ag-M-P-S chemical spaces. An efficient tiered screening strategy was developed that combines topological analysis with ab initio molecular dynamics simulations to rapidly exclude candidates unlikely to satisfy the stringent conductivity requirements of lithium superionic conductors. In particular, we find Li3Y(PS4)2 to be an extremely promising candidate exhibiting a room-temperature Li+ conductivity of 2.16 mS/cm, which can be increased multi-fold to 7.14 mS/cm and 5.25 mS/cm via aliovalent doping with Ca2+ and Zr4+, respectively. More critically, we show that the phase and electrochemical stability of Li3Y(PS4)2 is expected to be better than current state-of-the-art lithium superionic conductors.
Magnetism and Faraday Rotation in Oxygen-Deficient Polycrystalline and Single-Crystal Iron-Substituted Strontium Titanate
Taichi Goto, Dong Hun Kim, Xueyin Sun, Mehmet C. Onbasli, Juan M. Florez, Shyue Ping Ong, Patricio Vargas, Karl Ackland, Plamen Stamenov, Nicolas M. Aimon, Mitsuteru Inoue, Harry L. Tuller, Gerald F. Dionne, J. Michael D. Coey, Caroline A. Ross
Physical Review Applied, 2017, 7, 2, 024006
Abstract
Both polycrystalline and single-crystal films of iron-substituted strontium titanate, SrðTi 0.65 Fe 0.35 ÞO 3−δ , prepared by pulsed laser deposition, show room-temperature magnetism and Faraday rotation, with the polycrystalline films exhibiting higher saturation magnetization and Faraday rotation. The magnetic properties vary with the oxygen pressure at which the films are grown, showing a maximum at pressures of approximately 4 μ Torr at which the unit-cell volume is largest. The results are discussed in terms of the oxygen stoichiometry and corresponding Fe valence states, the structure and strain state, and the presence of small-volume fractions of metallic Fe in single-crystal films grown at the optimum deposition pressure. Integration of magneto-optical polycrystalline films on an optical-waveguide device demonstrates a nonreciprocal phase shift.
Data-Driven First-Principles Methods for the Study and Design of Alkali Superionic Conductors
Zhi Deng, Zhuoying Zhu, Iek-Heng Chu, Shyue Ping Ong
Chemistry of Materials, 2017, 29, 1, 281–288
Abstract
We present a detailed exposition of how first principles methods can be used to guide alkali superionic conductor (ASIC) study and design. Using the argyrodite Li6PS5Cl as a case study, we demonstrate how modern information technology (IT) infrastructure and software tools can facilitate the assessment of alkali superionic conductors in terms of various critical properties of interest such as phase and electrochemical stability, and ionic conductivity. The emphasis is on well-documented, reproducible analysis code that can be readily generalized to other materials systems and design problems. For our chosen Li6PS5Cl case study material, we show that Li excess is crucial to enhancing its conductivity by increasing the occupancy of interstitial sites that promote long-range Li+ diffusion between cage-like frameworks. The predicted room-temperature conductivities and activation barriers are in reasonably good agreement with experimental values.
Comparison of the Polymorphs of VOPO4 as Multi-Electron Cathodes for Rechargeable Alkali-Ion Batteries
Yuh-Chieh Lin, Marc F. V. Hidalgo, Iek-Heng' Chu, Natasha A. Chernova, M. Stanley Whittingham, Shyue Ping Ong
J. Mater. Chem. A, 2017, 33
Abstract
Multi-electron polyanion cathodes offer the potential for achieving both high voltage and high capacity in rechargeable alkali-ion batteries. Among the few materials known to exhibit multi-electron cycling, the polymorphs of VOPO4, which operate on the V3+–V4+–V5+ redox couples, are particularly promising due to the high gravimetric capacities that have been achieved and the high voltage of the V4+/5+ couple. In this work, we performed a systematic first principles investigation, supported by careful electrochemical characterization and published experimental data, of the relative thermodynamic stability, voltage, band gap, and diffusion kinetics for alkali intercalation into the β, ε and αI polymorphs of VOPO4. We find that all VOPO4 polymorphs remain reasonably stable with the insertion of one alkali ion per V, but are significantly destabilized with the insertion of two alkali ions per V. The voltages for Na insertion are ∼0.33–0.69 V lower than those for Li insertion. We find that the αI polymorph is predicted to have higher Li+ migration barriers and larger band gaps than the β and ε polymorphs, which account for the relatively worse electrochemical cycling performance observed. On the other hand, only the αI polymorph exhibits reasonably low barriers for Na+ migration compared to the β and ε polymorphs, which are consistent with observed electrochemical performances reported thus far in the literature. We also show that differences in the voltage, kinetics and rate capability of these different polymorphs for Li and Na insertion can be traced back to their fundamentally different VO6/VO5–PO4 frameworks.
Room-Temperature All-solid-state Rechargeable Sodium-ion Batteries with a Cl-doped Na3PS4 Superionic Conductor
Iek-Heng Chu, Christopher S. Kompella, Han Nguyen, Zhuoying Zhu, Sunny Hy, Zhi Deng, Ying Shirley Meng, Shyue Ping Ong
Scientific Reports, 2016, 6, 1, 33733
Abstract
All-solid-state sodium-ion batteries are promising candidates for large-scale energy storage applications. The key enabler for an all-solid-state architecture is a sodium solid electrolyte that exhibits high Na+ conductivity at ambient temperatures, as well as excellent phase and electrochemical stability. In this work, we present a first-principles-guided discovery and synthesis of a novel Cl-doped tetragonal Na3PS4 (t-Na3−xPS4−xClx) solid electrolyte with a room-temperature Na + conductivity exceeding 1 mS cm−1 . We demonstrate that an all-solid-state TiS2/t-Na3−xPS4−xClx/Na cell utilizing this solid electrolyte can be cycled at room-temperature at a rate of C/10 with a capacity of about 80 mAh g−1 over 10 cycles. We provide evidence from density functional theory calculations that this excellent electrochemical performance is not only due to the high Na+ conductivity of the solid electrolyte, but also due to the effect that “salting” Na3PS4 has on the formation of an electronically insulating, ionically conducting solid electrolyte interphase.
An integrated first principles and experimental investigation of the relationship between structural rigidity and quantum efficiency in phosphors for solid state lighting
Jungmin Ha, Zhenbin Wang, Ekaterina Novitskaya, Gustavo A. Hirata, Olivia A. Graeve, Shyue Ping Ong, Joanna McKittrick
Journal of Luminescence, 2016, 179, 297–305
Abstract
In this paper, we outline an integrated approach for exploring novel near-UV excited phosphors. To test the hypothesis of whether high host structural rigidity results in phosphors with high quantum efficiency (Φ), we calculated the Debye temperatures (Θ) for 27 host materials using density functional theory calculations. We identified Eu2+- activated Ca7Mg(SiO4)4 and CaMg(SiO3)2 as having a relatively high Θ = 601 K and 665 K, respectively, and predicted excitation energies of 3.18 eV (337 nm) and 3.29 eV (377 nm), respectively, both of which are in good agreement with the results of photoluminescence spectroscopy. However, the measured Φ for these two phosphors was \textless 30%, which indicates that Θ alone is not a sufficient condition for a high Φ. This work demonstrates the potential of combined first-principles calculations and experiments in the discovery and design of novel near-UV excited phosphors.
Elastic Properties of Alkali Superionic Conductor Electrolytes from First Principles Calculations
Zhi Deng, Zhenbin Wang, Iek-Heng Chu, Jian Luo, Shyue Ping Ong
Journal of The Electrochemical Society, 2016, 163, 2, A67–A74
Abstract
In thiswork,we present a comprehensive investigation of the elastic properties (the full elastic tensor, bulk, shear and Young's moduli, and Poisson's ratio) of 23 well-known ceramic alkali superionic conductor electrolytes (SICEs) using first principles calculations. We find that the computed elastic moduli are in good agreement with experimental data (wherever available) and chemical bonding nature.The anion species and structural framework have a significant influence on the elastic properties, and the relative elastic moduli of the various classes of SICEs follow the order thiophosphate \textless antiperovskite \textless phosphate \textless NASICON \textless garnet \textless perovskite. Within the same framework structure, we observe that Na SICEs are softer than their Li analogs. We discuss the implications of these findings in the context of fabrication, battery operation, and enabling a Li metal anode. The data computed in this work will also serve as a useful reference for future experiments as well as theoretical modeling of SICEs for rechargeable alkali-ion batteries.
The thermodynamic scale of inorganic crystalline metastability
Wenhao Sun, Stephen T Dacek, Shyue Ping Ong, Geoffroy Hautier, Anubhav Jain, William D Richards, Anthony C Gamst, Kristin A Persson, Gerbrand Ceder
Science Advances, 2016, 2, 11, e1600225–e1600225
Abstract
The space of metastable materials offers promising new design opportunities for next-generation technological materials such as complex oxides, semiconductors, pharmaceuticals, steels, and beyond. Although metastable phases are ubiquitous in both nature and technology, only a heuristic understanding of their underlying ther- modynamics exists. We report a large-scale data-mining study of the Materials Project, a high-throughput database of density functional theory–calculated energetics of Inorganic Crystal Structure Database structures, to explicitly quantify the thermodynamic scale of metastability for 29,902 observed inorganic crystalline phases. We reveal the influence of chemistry and composition on the accessible thermodynamic range of crystalline meta- stability for polymorphic and phase-separating compounds, yielding new physical insights that can guide the design of novel metastable materials. We further assert that not all low-energy metastable compounds can necessarily be synthesized, and propose a principle of ‘remnant metastability'—that observable metastable crystalline phases are generally remnants of thermodynamic conditions where they were once the lowest free-energy phase.
Surface energies of elemental crystals
Richard Tran, Zihan Xu, Balachandran Radhakrishnan, Donald Winston, Wenhao Sun, Kristin A. Persson, Shyue Ping Ong
Scientific Data, 2016, 3, 160080
Abstract
The surface energy is a fundamental property of the different facets of a crystal that is crucial to the understanding of various phenomena like surface segregation, roughening, catalytic activity, and the crystal's equilibrium shape. Such surface phenomena are especially important at the nanoscale, where the large surface area to volume ratios leads to properties that are significantly different from the bulk. In this work, we present the largest database of the calculated surface energies of elemental crystals to date. This database contains the surface energies of more than 100 polymorphs of about 70 elements, up to a maximum Miller index of two and three for non-cubic and cubic crystals, respectively. Well-known reconstruction schemes are also accounted for. The database is systematically improvable and has been rigorously validated against previous experimental and computational data where available. We will describe the methodology used in constructing the database, and how it can be accessed for further studies and design of materials.
Experimental and Computational Evaluation of a Sodium-Rich Anti-Perovskite for Solid State Electrolytes
Han Nguyen, Sunny Hy, Erik Wu, Zhi Deng, Mojtaba Samiee, Thomas Yersak, Jian Luo, Shyue Ping Ong, Ying Shirley Meng
Journal of The Electrochemical Society, 2016, 163, 10, A2165–A2171
Abstract
In this study we experimentally investigated the effects of two processing techniques on the sodium-rich anti-perovskite, Na3OBr; namely, conventional cold pressing (CP) and spark plasma sintering (SPS). We demonstrated that the electrolyte can be synthesized via a single-step solid state reaction. We compared the CP and SPS processed samples using XRD, SEM, and EIS. From these analyses it was found that SPS reduced Na3OBr's interfacial impedance by three orders of magnitude, which translated into an increase in the overall ionic conductivity and a reduction in the activation energy, from 1.142 eV to 0.837 eV. DFT was used to probe the mechanisms for ionic transport in Na-rich Na3OBr. The formation energies of ion diffusion-facilitating defects in Na3OBr were found to be much higher compared to the lithium-rich anti-perovskites (LiRAP), which can explain the difference in overall ionic conductivity between the two.
Uniform second Li ion intercalation in solid state ϵ-LiVOPO4
Linda W. Wangoh, Shawn Sallis, Kamila M. Wiaderek, Yuh-Chieh Lin, Bohua Wen, Nicholas F. Quackenbush, Natasha A. Chernova, Jinghua Guo, Lu Ma, Tianpin Wu, Tien-Lin Lee, Christoph Schlueter, Shyue Ping Ong, Karena W. Chapman, M. Stanley Whittingham, Louis F. J. Piper
Applied Physics Letters, 2016, 109, 5, 053904
Abstract
Full, reversible intercalation of two Li+ has not yet been achieved in promising VOPO4 electrodes. A pronounced Li+ gradient has been reported in the low voltage window (i.e. second lithium reaction) that is thought to originate from disrupted kinetics in the high voltage regime (i.e first lithium reaction). Here we employ a combination of hard and soft x–ray photoelectron and absorption spectroscopy techniques to depth profile solid state synthesized LiVOPO4 cycled within the low voltage window only. Analysis of the vanadium environment revealed no evidence of a Li+ gradient, which combined with almost full theoretical capacity confirms that disrupted kinetics in the high voltage window are responsible for hindering full two lithium insertion. Furthermore, we argue that the uniform Li+ intercalation is a prerequisite for the formation of intermediate phases Li1.50VOPO4 and Li1.75VOPO4 . The evolution from LiVOPO4 to Li2VOPO4 via the intermediate phases is confirmed by direct comparison between O K–edge absorption spectroscopy and density functional theory.
Electronic Structure Descriptor for the Discovery of Narrow-Band Red-Emitting Phosphors
Zhenbin Wang, Iek-Heng Chu, Fei Zhou, Shyue Ping Ong
Chemistry of Materials, 2016, 28, 11, 4024–4031
Abstract
Narrow-band red-emitting phosphors are a critical component in phosphor-converted light-emitting diodes for highly efficient illumination-grade lighting. In this work, we report the discovery of a quantitative descriptor for narrow-band Eu2+-activated emission identified through a comparison of the electronic structure of known narrow-band and broad-band phosphors. We find that a narrow emission bandwidth is characterized by a large splitting of more than 0.1 eV between the two highest Eu2+ 4f7 bands. By incorporating this descriptor in a high throughput first principles screening of 2,259 nitride compounds, we identify five promising new nitride hosts for Eu2+-activated red-emitting phosphors that are predicted to exhibit good chemical stability, thermal quenching resistance and quantum efficiency, as well as narrow-band emission. Our findings provide important insights into the emission characteristics of rare-earth activators in phosphor hosts, and a general strategy to the discovery of phosphors with a desired emission peak and bandwidth.
Molybdenum Substituted Vanadyl Phosphate ϵ-VOPO4 with Enhanced Two-Electron Transfer Reversibility and Kinetics for Lithium-Ion Batteries
Bohua Wen, Qi Wang, Yuhchieh Lin, Natasha A. Chernova, Khim Karki, Youngmin Chung, Fredrick Omenya, Shawn Sallis, Louis F. J. Piper, Shyue Ping Ong, M. Stanley Whittingham
Chemistry of Materials, 2016, 28, 9, 3159–3170
Abstract
We have investigated the possibility of molybdenum substitution into ϵ-VOPO 4 structure and its effects on the electrochemical performance of this material as a cathode in Li-ion battery. We have found that up to 5% of Mo can substitute V upon hydrothermal synthesis at 180 °C with further annealing at 550 °C. The substitution is confirmed by the increase of the unit cell volume with Mo content. A combination of X-ray absorption and photoelectron spectroscopy, magnetic studies, and density functional theory calculations indicates an Mo 6+ oxidation state which is charge compensated by reduction of the same amount of V to 4+. Mo- substituted samples show much smaller particle size as compared to unsubstituted ϵ-VOPO4 and significantly improved electrochemical behavior. ϵ-V0.95Mo0.05OPO4 shows the initial reversible capacity ∼250 mAh/g (∼1.6 Li) and ∼80% retention for up to 20 cycles at C/25. Sloping voltage profile, faster kinetics, and lower voltage hysteresis of Mo substituted VOPO4 are demonstrated by the galvanostatic intermittent titration technique. This enhanced electrochemical performance is attributed to the smaller particles and possible existence of partial LixMoyV1−yOPO4 solid solution supported by X-ray diffraction, which leads to less abrupt and completely reversible structure changes upon Li cycling evidenced by X-ray absorption spectroscopy.
New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships
Anubhav Jain, Geoffroy Hautier, Shyue Ping Ong, Kristin Persson
Journal of Materials Research, 2016, 31, 08, 977–994
Abstract
Data mining has revolutionized sectors as diverse as pharmaceutical drug discovery, finance, medicine, and marketing, and has the potential to similarly advance materials science. In this paper, we describe advances in simulation-based materials databases, open-source software tools, and machine learning algorithms that are converging to create new opportunities for materials informatics. We discuss the data mining techniques of exploratory data analysis, clustering, linear models, kernel ridge regression, tree-based regression, and recommendation engines. We present these techniques in the context of several materials application areas, including compound prediction, Li-ion battery design, piezoelectric materials, photocatalysts, and thermoelectric materials. Finally, we demonstrate how new data and tools are making it easier and more accessible than ever to perform data mining through a new analysis that learns trends in the valence and conduction band character of compounds in the Materials Project database using data on over 2500 compounds.
Aqueous Stability of Alkali Superionic Conductors from First-Principles Calculations
Balachandran Radhakrishnan, Shyue Ping Ong
Frontiers in Energy Research, 2016, 4, 16
Abstract
Ceramic alkali superionic conductor solid electrolytes (SICEs) play a prominent role in the development of rechargeable alkali-ion batteries, ranging from replacement of organic electrolytes to being used as separators in aqueous batteries. The aqueous stability of SICEs is an important property in determining their applicability in various roles. In this work, we analyze the aqueous stability of twelve well-known Li-ion and Na-ion SICEs using Pourbaix diagrams constructed from first principles calculations. We also introduce a quantitative free energy measure to compare the aqueous stability of SICEs under different environments. Our results show that though oxides are in general more stable in aqueous environments than sulfides and halide-containing chemistries, the cations present play a crucial role in determining whether solid phases are formed within the voltage and pH ranges of interest.
Design and synthesis of the superionic conductor Na10SnP2S12
William Davidson Richards, Tomoyuki Tsujimura, Lincoln J. Miara, Yan Wang, Jae Chul Kim, Shyue Ping Ong, Ichiro Uechi, Naoki Suzuki, Gerbrand Ceder
Nature Communications, 2016, 7, 11009
Abstract
Sodium-ion batteries are emerging as candidates for large-scale energy storage due to their low cost and the wide variety of cathode materials available. As battery size and adoption in critical applications increases, safety concerns are resurfacing due to the inherent flamm- ability of organic electrolytes currently in use in both lithium and sodium battery chemistries. Development of solid-state batteries with ionic electrolytes eliminates this concern, while also allowing novel device architectures and potentially improving cycle life. Here we report the computation-assisted discovery and synthesis of a high-performance solid-state electrolyte material: Na 10 SnP 2 S 12 , with room temperature ionic conductivity of 0.4 mS cm À rivalling the conductivity of the best sodium sulfide solid electrolytes to date. We also computationally investigate the variants of this compound where tin is substituted by ger- manium or silicon and find that the latter may achieve even higher conductivity.
Computational studies of solid-state alkali conduction in rechargeable alkali-ion batteries
Zhi Deng, Yifei Mo, Shyue Ping Ong
NPG Asia Materials, 2016, 8, 3, e254
Abstract
The facile conduction of alkali ions in a crystal host is of crucial importance in rechargeable alkali-ion batteries, the dominant form of energy storage today. In this review, we provide a comprehensive survey of computational approaches to study solid-state alkali diffusion. We demonstrate how these methods have provided useful insights into the design of materials that form the main components of a rechargeable alkali-ion battery, namely the electrodes, superionic conductor solid electrolytes and interfaces. We will also provide a perspective on future challenges and directions. The scope of this review includes the monovalent lithium- and sodium-ion chemistries that are currently of the most commercial interest.
Insights into the Performance Limits of the Li7P3S11 Superionic Conductor: A Combined First-Principles and Experimental Study
Iek-Heng Chu, Han Nguyen, Sunny Hy, Yuh-Chieh Lin, Zhenbin Wang, Zihan Xu, Zhi Deng, Ying Shirley Meng, Shyue Ping Ong
ACS Applied Materials & Interfaces, 2016, 8, 12, 7843–7853
Abstract
The Li7P3S11 glass-ceramic is a promising superionic conductor electrolyte (SCE) with an extremely high Li + conductivity that exceeds that of even traditional organic electrolytes. In this work, we present a combined computational and experimental investigation of the material performance limitations in terms of its phase and electro- chemical stability, and Li+ conductivity. We find that Li7P3S11 is metastable at 0 K but becomes stable at above 630 K (∼360 °C) when vibrational entropy contributions are accounted for, in agreement with differential scanning calorimetry measurements. Both scanning electron microscopy and the calculated Wulff shape show that Li7P3S11 tends to form relatively isotropic crystals. In terms of electrochemical stability, first-principles calculations predict that, unlike the LiCoO2 cathode, the olivine LiFePO4 and spinel LiMn2O4 cathodes are likely to form stable passivation interfaces with the Li7P3S11 SCE. This finding underscores the importance of considering multicomponent integration in developing an all-solid-state architecture. To probe the fundamental limit of its bulk Li+ conductivity, a comparison of conventional cold-press sintered versus spark-plasma sintering (SPS) Li7P3S11 was done in conjunction with ab initio molecular dynamics (AIMD) simulations. Though the measured diffusion activation barriers are in excellent agreement, the AIMD- predicted room-temperature Li+ conductivity of 57 mS cm−1 is much higher than the experimental values. The optimized SPS sample exhibits a room-temperature Li+ conductivity of 11.6 mS cm−, significantly higher than that of the cold-pressed sample (1.3 mS cm−1) due to the reduction of grain boundary resistance by densification. We conclude that grain boundary conductivity is limiting the overall Li+ conductivity in Li7P3S11, and further optimization of overall conductivities should be possible. Finally, we show that Li+ motions in this material are highly collective, and the flexing of the P2S7 ditetrahedra facilitates fast Li+ diffusion.
Thermal Stability and Reactivity of Cathode Materials for Li-Ion Batteries
Yiqing Huang, Yuh-Chieh Lin, David M. Jenkins, Natasha A. Chernova, Youngmin Chung, Balachandran Radhakrishnan, Iek-Heng Chu, Jin Fang, Qi Wang, Fredrick Omenya, Shyue Ping Ong, M. Stanley Whittingham
ACS Applied Materials & Interfaces, 2016, 8, 11, 7013–7021
Abstract
The thermal stability of electrochemically delithiated Li0.1Ni0.8Co0.15Al0.05O2 (NCA), FePO4 (FP), Mn0.8Fe0.2PO4 (MFP), hydrothermally synthesized VOPO4, LiVOPO4 and electrochemically lithiated Li2VOPO4 is investigated by differential scanning calorimetry (DSC) and thermogravimetric analysis, coupled with mass spectrometry (TGA-MS). The thermal stability of the delithiated materials is found to be in the order: NCA\textless VOPO4\textless MFP\textless FP. Unlike the layered oxides and MFP, VOPO4 does not evolve O2 on heating. Thus VOPO4 is less likely to cause a thermal runaway in batteries at elevated temperature, and so is inherently safer. The lithiated materials LiVOPO4, Li2VOPO4 and LiNi0.8Co0.15Al0.05O2 are found to be stable in the presence of electrolyte, but sealed capsule high-pressure experiments show a phase transformation of VOPO4 → HVOPO4 → H2VOPO4 when VOPO4 reacts with electrolyte (1 M LiPF6 in EC: DMC=1:1) between 200 and 300 °C. Using first principles calculations, we confirm that the charged VOPO4 cathode is indeed predicted to be marginally less stable than FP, but significantly more stable than NCA in the absence of electrolyte. An analysis of the reaction equilibria between VOPO4 and EC using a multi-component phase diagram approach yields products and reaction enthalpies that are highly consistent with the experiment results.
Thermodynamics, Kinetics and Structural Evolution of ϵ-LiVOPO4 over Multiple Lithium Intercalation
Yuhchieh Lin, Bohua Wen, Kamila M. Wiaderek, Shawn Sallis, Hao Liu, Saul H. Lapidus, Olaf J. Borkiewicz, Nicholas F. Quackenbush, Natasha A. Chernova, Khim Karki, Fredrick Omenya, Peter J. Chupas, Louis F. J. Piper, M. Stanley Whittingham, Karena W. Chapman, Shyue Ping Ong
Chemistry of Materials, 2016, 28, 6, 1794–1805
Abstract
In this work, we demonstrate the stable cycling of more than one Li in solid-state-synthesized ϵ-LiVOPO4 over more than 20 cycles for the first time. Using a combination of density functional theory (DFT) calculations, X-ray pair distribution function (PDF) analysis and X-ray Absorption Near Edge Structure (XANES) measurements, we present a comprehensive analysis of the thermodynamics, kinetics and structural evolution of ϵ-LixVOPO4 over the entire lithiation range. We identify two intermediate phases at x = 1.5 and 1.75 in the low-voltage regime using DFT calculations, and the computed and electrochemical voltage profiles are in excellent agreement. Operando PDF and EXAFS techniques show a reversible hysteretic change in the short (\textless 2 \AA) V-O bond lengths coupled with an irreversible extension of the long V-O bond (\textgreater 2.4 \AA) during low-voltage cycling. Hydrogen intercalation from electrolyte decomposition is a possible explanation for the ∼ 2.4 \AA V-O bond and its irreversible extension. Finally, we show that LixVOPO4 is likely a pseudo-1D ionic diffuser with low electronic conductivity using DFT calculations, which suggests that nano-sizing and carbon coating is necessary to achieve good electrochemical performance in this material.
Elucidating Structure–Composition–Property Relationships of the β-SiAlON:Eu2+ Phosphor
Zhenbin Wang, Weike Ye, Iek-Heng Chu, Shyue Ping Ong
Chemistry of Materials, 2016, 28, 23, 8622–8630
Abstract
In this work, we performed a systematic inves-tigation of structure−composition−property relationships in Eu 2+ -activated β-SiAlON, one of the most promising narrow-band green phosphors for high-power light-emitting diodes and liquid crystal display backlighting with wide color gamut. Using first-principles calculations, we identified and confirmed various chemical rules for Si−Al, O−N, and Eu activator ordering within the β-SiAlON structure. Through the construction of energetically favorable models based on these chemical rules, we studied the effect of oxygen content and Eu 2+ activator concentrations on the local EuN 9 activator environment, and its impact on important photoluminescence properties such as emission peak position (using the band gap as a proxy), bandwidth, and thermal quenching resistance. Increasing oxygen content is shown to lead to an increase in Eu−N bond lengths and distortion of the EuN 9 coordination polyhedron, modifying the crystal field environment of the Eu 2+ activator, and resulting in red-shifting and broadening of the emission. We also show that the calculated excited band structure of β-SiAlON exhibits a large gap between the 5d levels and the conduction band of the host, indicating a large barrier toward thermal ionization (\textgreater0.5 eV) and, hence, excellent thermal quenching stability. Based on these insights, we discuss potential strategies for further composition optimization of β-SiAlON.
Large scale computational screening and experimental discovery of novel materials for high temperature CO2 capture
Matthew T. Dunstan, Anubhav Jain, Wen Liu, Shyue Ping Ong, Tao Liu, Jeongjae Lee, Kristin A. Persson, Stuart A. Scott, John S. Dennis, Clare P. Grey
Energy Environ. Sci., 2016, 9, 4, 1346–1360
Abstract
The implementation of large-scale carbon dioxide capture and storage (CCS) is dependent on finding materials that satisfy several different criteria, the most important being minimising the energy load imposed on the power plant to run the process. The most mature CCS technology, amine scrubbing, leads to a loss of 30% of the electrical work output of the power station without capture, which is far too high for widespread deployment. High-temperature CO 2 absorption looping has emerged as a technology that has the potential to deliver much lower energy penalties, but further work is needed to find and develop an optimal material. We have developed a combined computational and experimental methodology to predict new materials that should have desirable properties for CCS looping, and then select promising candidates to experimentally validate these predictions. This work not only has discovered novel materials for use in high-temperature CCS looping, but analysis of the entirety of the screening enables greater insights into new design strategies for future development.
Computational study of metallic dopant segregation and embrittlement at Molybdenum grain boundaries
Richard Tran, Zihan Xu, Naixie Zhou, Balachandran Radhakrishnan, Jian Luo, Shyue Ping Ong
Acta Materialia, 2016, 117, 91–99
Abstract
Mo and its alloys have been widely used as refractory materials owing to their excellent high temperature properties, but a critical limitation is their low ductility. Doping the grain boundaries (GBs) of Mo with metals such as Zr or Al have previously been demonstrated as a promising approach to address this shortcoming, whereas other alloy elements are known to embrittle the GBs. In this work, we investigated the segregation and strengthening/embrittling effects of 29 metallic dopants at the Σ5(310) tilt and Σ5(100) twist Mo GBs using density functional theory (DFT) calculations and empirical continuum models. In agreement with previous works for other metals, we find that the strain, as measured by the relative metallic radius versus Mo, is a good predictor of the segregation tendency, while the difference in cohesive energies between the dopant and Mo is a good predictor of the strengthening/embrittling effect. However, we find that dopant chemistry also plays a significant role in affecting segregation behavior at GBs, particularly in driving the formation of intermetallic precipitates or 2-D interfacial phases (complexions). We also show that the site preference of a dopant in the GB can lead to strengthening effects that deviate from those predicted using simple bond-breaking arguments. Assuming a fast cleavage model of fracture, Ta, Re, Os and W are predicted to have a weak strengthening effect on Mo for the Σ5(310) tilt GB, and Mn, Fe, Co and Nb are predicted to have reasonable strengthening effects for the Σ5(100) twist GB.
Interfacial Effects in ϵ-LixVOPO4 and Evolution of the Electronic Structure
Nicholas F. Quackenbush, L. Wangoh, D. O. Scanlon, R. Zhang, Y. Chung, Z. Chen, Bohua Wen, Yuh-Chieh Lin, J. C. Woicik, Natasha A. Chernova, Shyue Ping Ong, M. Stanley Whittingham, Louis F. J. Piper
Chemistry of Materials, 2015, 27, 24, 8211–8219
Abstract
The epsilon polymorph of vanadyl phosphate ϵ- VOPO4 is a promising cathode material for high-capacity Li ion batteries, owing to its demonstrated ability to reversibly incorporate two lithium ions per redox center. As lithium is inserted into the nanosized particles within the cathode, the electrochemical reaction can be largely affected by the interfacial chemistry at the nanoparticle surface. We performed X-ray photoelectron spectroscopy using both soft (XPS) and hard (HAXPES) X-rays to chemically distinguish and depth-resolve the interfacial phase transitions in ϵ-VOPO4 electrodes as a function of electrochemical discharge. Our analysis shows that the second lithium reaction begins before the full incorporation of the first lithium. This results in a pronounced lithium gradient within the nanoparticles, with the ϵ-Li2VOPO4 phase only forming near the surface. These results indicate that a disruption of the kinetics are limiting the realized capacity in our hydrothermally synthesized ϵ-VOPO4. Moreover, from inspection of the valence band region, we were able to monitor the evolution of ϵ-VOPO4 to ϵ-Li2VOPO4 at the surface of our nanoparticles. These assignments are confirmed by hybrid density functional theory of the three end phases.
Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening
Lei Cheng, Rajeev S. Assary, Xiaohui Qu, Anubhav Jain, Shyue Ping Ong, Nav Nidhi Rajput, Kristin Persson, Larry A. Curtiss
The Journal of Physical Chemistry Letters, 2015, 6, 283–291
Abstract
Computational screening techniques have been found to be an effective alternative to the trial and error of experimentation for discovery of new materials. With increased interest in development of advanced electrical energy storage systems, it is essential to find new electrolytes that function effectively. This Perspective reviews various methods for screening electrolytes and then describes a hierarchical computational scheme to screen multiple properties of advanced electrical energy storage electrolytes using highthroughput quantum chemical calculations. The approach effectively down-selects a large pool of candidates based on successive property evaluation. As an example, results of screening are presented for redox potentials, solvation energies, and structural changes of ∼1400 organic molecules for nonaqueous redox flow batteries. Importantly, on the basis of high-throughput screening, in silico design of suitable candidate molecules for synthesis and electrochemical testing can be achieved. We anticipate that the computational approach described in this Perspective coupled with experimentation will have a significant role to play in the discovery of materials for future energy needs.
FireWorks: a dynamic workflow system designed for high-throughput applications
Anubhav Jain, Shyue Ping Ong, Wei Chen, Bharat Medasani, Xiaohui Qu, Michael Kocher, Miriam Brafman, Guido Petretto, Gian-Marco Rignanese, Geoffroy Hautier, Daniel Gunter, Kristin A. Persson
Concurrency and Computation: Practice and Experience, 2015, 27, 17, 5037–5059
Abstract
This paper introduces FireWorks, a workflow software for running high-throughput calculation workflows at supercomputing centers. FireWorks has been used to complete over 50 million CPU-hours worth of computational chemistry and materials science calculations at the National Energy Research Supercomputing Center. It has been designed to serve the demanding high-throughput computing needs of these applications, with extensive support for (i) concurrent execution through job packing, (ii) failure detection and correction, (iii) provenance and reporting for long-running projects, (iv) automated duplicate detection, and (v) dynamic workflows (i.e., modifying the workflow graph during runtime). We have found that these features are highly relevant to enabling modern data-driven and high-throughput science applications, and we discuss our implementation strategy that rests on Python and NoSQL databases (MongoDB). Finally, we present performance data and limitations of our approach along with planned future work.
Role of Na+ Interstitials and Dopants in Enhancing the Na+ Conductivity of the Cubic Na3PS4 Superionic Conductor
Zhuoying Zhu, Iek-Heng Chu, Zhi Deng, Shyue Ping Ong
Chemistry of Materials, 2015, 27, 24, 8318–8325
Abstract
In this work, we performed a first-principles investigation of the phase stability, dopant formation energy and Na + conductivity of pristine and doped cubic Na3PS4 (c-Na3PS4). We show that pristine c-Na3PS4 is an extremely poor Na ionic conductor, and the introduction of Na + excess is the key to achieving reasonable Na + conductivities. We studied the effect of aliovalent doping of M4+ for P5+ in c-Na3PS4, yielding Na3+xMxP1−xS4 (M = Si, Ge, and Sn with x = 0.0625; M = Si with x = 0.125). The formation energies in all the doped structures with dopant concentration of x = 0.0625 are found to be relatively low. Using ab initio molecular dynamics simulations, we predict that 6.25% Si-doped c-Na3PS4 has a Na+ conductivity of 1.66 mS/cm, in excellent agreement with previous experimental results. Remarkably, we find that Sn4+ doping at the same concentration yields a much higher predicted Na+ conductivity of 10.7 mS/cm, though with a higher dopant formation energy. A higher Si4+ doping concentration of x = 0.125 also yields a significant increase in Na+ conductivity with an even higher dopant formation energy. Finally, topological and van Hove correlation function analyses suggest that the channel volume and correlation in Na+ motions may play important roles in enhancing Na+ conductivity in this structure.
Design principles for solid-state lithium superionic conductors
Yan Wang, William Davidson Richards, Shyue Ping Ong, Lincoln J. Miara, Jae Chul Kim, Yifei Mo, Gerbrand Ceder
Nature Materials, 2015, 14, 10, 1026–1031
Abstract
Lithium solid electrolytes can potentially address two key limitations of the organic electrolytes used in today's lithium-ion batteries, namely, their flammability and limited electrochemical stability. However, achieving a Li+ conductivity in the solid state comparable to existing liquid electrolytes (\textgreater1 mS cm−1) is particularly challenging. In this work, we reveal a fundamental relationship between anion packing and ionic transport in fast Li-conducting materials and expose the desirable structural attributes of good Li-ion conductors. We find that an underlying body-centred cubic-like anion framework, which allows direct Li hops between adjacent tetrahedral sites, is most desirable for achieving high ionic conductivity, and that indeed this anion arrangement is present in several known fast Li-conducting materials and other fast ion conductors. These findings provide important insight towards the understanding of ionic transport in Li-ion conductors and serve as design principles for future discovery and design of improved electrolytes for Li-ion batteries.
The Electrolyte Genome project: A big data approach in battery materials discovery
Xiaohui Qu, Anubhav Jain, Nav Nidhi Rajput, Lei Cheng, Yong Zhang, Shyue Ping Ong, Miriam Brafman, Edward Maginn, Larry a. Curtiss, Kristin a. Persson
Computational Materials Science, 2015, 103, 56–67
Abstract
We present a high-throughput infrastructure for the automated calculation of molecular properties with a focus on battery electrolytes. The infrastructure is largely open-source and handles both practical aspects (input file generation, output file parsing, and information management) as well as more complex problems (structure matching, salt complex generation, and failure recovery). Using this infrastructure, we have computed the ionization potential (IP) and electron affinities (EA) of 4830 molecules relevant to battery electrolytes (encompassing almost 55,000 quantum mechanics calculations) at the B3LYP/ 6-31+G⁄ level. We describe automated workflows for computing redox potential, dissociation constant, and salt-molecule binding complex structure generation. We present routines for automatic recovery from calculation errors, which brings the failure rate from 9.2% to 0.8% for the QChem DFT code. Automated algorithms to check duplication between two arbitrary molecules and structures are described. We present benchmark data on basis sets and functionals on the G2-97 test set; one finding is that a IP/EA calculation method that combines PBE geometry optimization and B3LYP energy evaluation requires less computational cost and yields nearly identical results as compared to a full B3LYP calculation, and could be suitable for the calculation of large molecules. Our data indicates that among the 8 functionals tested, XYGJ-OS and B3LYP are the two best functionals to predict IP/EA with an RMSE of 0.12 and 0.27 eV, respectively. Application of our automated workflow to a large set of quinoxaline derivative molecules shows that functional group effect and substitution position effect can be separated for IP/EA of quinoxaline derivatives, and the most sensitive position is different for IP and EA.
Rational Composition Optimization of the Lithium-Rich Li3OCl1–xBrx Anti-Perovskite Superionic Conductors
Zhi Deng, Balachandran Radhakrishnan, Shyue Ping Ong
Chemistry of Materials, 2015, 27, 10, 3749–3755
Abstract
The newly discovered lithium-rich antiperovskite (LRAP) superionic conductors are an extremely interesting class of materials with potential applications as solid electrolytes in Li-ion batteries. In this work, we present a rational composition optimization strategy for maximizing the Li+ conductivity in the LRAP guided by a combination of firstprinciples calculations and percolation theory. Using nudged elastic band (NEB) calculations, we show that a Cl-rich channel with Br-rich end points configuration leads to low vacancy migration barriers in the LRAP structure. By incorporating the halide-environment-dependent NEB barriers in a bond percolation model, we predict that there are potentially higher conductivity Li3OCl1−xBrx structures near 0.235 ≤ x ≤ 0.395. This prediction is confirmed by AIMD simulation that finds Li3OCl0.75Br0.25 to have a higher Li+ conductivity than Li3OCl0.5Br0.5, the highest conductivity LRAP identified experimentally thus far. These results highlight that there is scope for further enhancing the conductivity in the LRAP chemistry. The general approach developed can potentially be extended to other ion-conducting systems, such as the structurally similar perovskite oxygen-ion conductors of interest in solid-oxide fuel cells as well as other superionic conductors.
The Materials Application Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles
Shyue Ping Ong, Shreyas Cholia, Anubhav Jain, Miriam Brafman, Dan Gunter, Gerbrand Ceder, Kristin a. Persson
Computational Materials Science, 2015, 97, 209–215
Abstract
In this paper, we describe the Materials Application Programming Interface (API), a simple, flexible and efficient interface to programmatically query and interact with the Materials Project database based on the REpresentational State Transfer (REST) pattern for the web. Since its creation in Aug 2012, the Materials API has been the Materials Project's de facto platform for data access, supporting not only the Materials Project's many collaborative efforts but also enabling new applications and analyses. We will highlight some of these analyses enabled by the Materials API, particularly those requiring consolidation of data on a large number of materials, such as data mining of structural and property trends, and generation of phase diagrams. We will conclude with a discussion of the role of the API in building a community that is developing novel applications and analyses based on Materials Project data.
Vacancy Ordering in O3-Type Layered Metal Oxide Sodium-Ion Battery Cathodes
Alexandra J. Toumar, Shyue Ping Ong, William Davidson Richards, Stephen Dacek, Gerbrand Ceder
Physical Review Applied, 2015, 4, 6, 064002
Abstract
Current state-of-the-art Na-ion battery cathodes are selected from the broad chemical space of layered first-row transition-metal (TM) oxides. Unlike their lithium-ion counterparts, seven first-row layered TM oxides can intercalate Na ions reversibly. Their voltage curves indicate significant and numerous reversible phase transformations during electrochemical cycling. These transformations are not yet fully understood but arise from Na-ion vacancy ordering and metal oxide slab glide. In this study, we investigate the nature of vacancy ordering within the O3 host lattice framework. We generate predicted electrochemical voltage curves for each of the Na-ion intercalating layered TM oxides by using a high-throughput framework of density-functional-theory calculations. We determine a set of vacancy-ordered phases appearing as ground states in all NaxMO2 systems and investigate the energy effect of the stacking of adjacent layers.
Relating voltage and thermal safety in Li-ion battery cathodes: a high-throughput computational study
Anubhav Jain, Geoffroy Hautier, Shyue Ping Ong, Stephen Dacek, Gerbrand Ceder
Phys. Chem. Chem. Phys., 2015, 17, 8, 5942–5953
Abstract
High voltage and high thermal safety are desirable characteristics of cathode materials, but difficult to achieve simultaneously. This work uses high-throughput density functional theory computations to evaluate the link between voltage and safety (as estimated by thermodynamic O2 release temperatures) for over 1400 cathode materials. Our study indicates that a strong inverse relationship exists between voltage and safety: just over half the variance in O2 release temperature can be explained by voltage alone. We examine the effect of polyanion group, redox couple, and ratio of oxygen to counter-cation on both voltage and safety. As expected, our data demonstrates that polyanion groups improve safety when comparing compounds with similar voltages. However, a counterintuitive result of our study is that polyanion groups produce either no benefit or reduce safety when comparing compounds with the same redox couple. Using our data set, we tabulate voltages and oxidation potentials for over 105 combinations of redox couple/anion, which can be used towards the design and rationalization of new cathode materials. Overall, only a few compounds in our study, representing limited redox couple/ polyanion combinations, exhibit both high voltage and high safety. We discuss these compounds in more detail as well as the opportunities for designing safe, high-voltage cathodes.
Insights into diffusion mechanisms in P2 layered oxide materials by first-principles calculations
Yifei Mo, Shyue Ping Ong, Gerbrand Ceder
Chemistry of Materials, 2014, 26, 18, 5208–5214
Abstract
Significant progress has been made in Na-intercalation compounds for rechargeable Na batteries. P2 NaMO2 layered oxides have been shown to have high capacity, good cyclability, and improved rate capability. In this study, we investigate the diffusion mechanism in P2 NaCoO2 using ab initio molecular dynamics simulations and nudged elastic band calculations. We identify the Na diffusion mechanisms in P2 NaCoO2 at non-dilute Na concentrations and illustrate the strong effect of Na-Na interactions on Na diffusion. Our computational results demonstrate that P2 sodium layered oxides are fast Na ionic conductors over a wide range of Na concentrations and are promising cathode materials with high rate capabilities.
Nanoscale stabilization of sodium oxides: implications for Na-O2 batteries
ShinYoung Kang, Yifei Mo, Shyue Ping Ong, Gerbrand Ceder
Nano letters, 2014, 14, 2, 1016–20
Abstract
The thermodynamic stability of materials can depend on particle size due to the competition between surface and bulk energy. In this Letter, we show that, while sodium peroxide (Na2O2) is the stable bulk phase of Na in an oxygen environment at standard conditions, sodium superoxide (NaO2) is considerably more stable at the nanoscale. As a consequence, the superoxide requires a much lower nucleation energy than the peroxide, explaining why it can be observed as the discharge product in some Na-O2 batteries. As the superoxide can be recharged (decomposed) at much lower overpotentials than the peroxide, these findings are important to create highly reversible Na-O2 batteries. We derive the specific electrochemical conditions to nucleate and retain Na-superoxides and comment on the importance of considering the nanophase thermodynamics when optimizing an electrochemical system.
Direct visualization of the Jahn–Teller effect coupled to Na ordering in Na5/8MnO2
Xin Li, Xiaohua Ma, Dong Su, Lei Liu, Robin Chisnell, Shyue Ping Ong, Hailong Chen, Alexandra Toumar, Juan-carlos Idrobo, Yuechuan Lei, Jianming Bai, Feng Wang, Je W Lynn, Young S Lee, Gerbrand Ceder
Nat Mater, 2014, AOP
Abstract
A Facile Mechanism for Recharging Li2O2 in Li–O2 Batteries
ShinYoung Kang, Yifei Mo, Shyue Ping Ong, Gerbrand Ceder
Chemistry of Materials, 2013, 25, 16, 3328–3336
Abstract
Li-air is a novel battery technology with the potential to offer very high specific energy, but which currently suffers from a large charging overpotential and low power density. In this work, we use ab initio calculations to demonstrate that a facile mechanism for recharging Li2O2 exists. Rather than the direct decomposition pathway of Li2O2 into Li and O2 suggested by equilibrium thermodynamics, we find an alternative reaction pathway based on topotactic delithiation of Li2O2 to form off-stoichiometric Li2-xO2 compounds akin to the charging mechanism in typical Li-ion intercalation electrodes. The low formation energy of bulk Li2-xO2 phases confirms that this topotactic delithiation mechanism is rendered accessible at relatively small overpotentials of ∼0.3–0.4 V and is likely to be kinetically favored over Li2O2 decomposition. Our findings indicate that at the Li2O2 particle level there are no obstacles to increase the current density, and point to an exciting opportunity to create fast charging Li-air systems.
Effect of Rb and Ta Doping on the Ionic Conductivity and Stability of the Garnet Li 7+2x–y (La 3–x Rb x)(Zr 2–y Ta y)O12 (0 ≤ x ≤ 0.375, 0 ≤ y ≤ 1) Superionic Conductor: A First Principles Investigation
Lincoln James Miara, Shyue Ping Ong, Yifei Mo, William Davidson Richards, Youngsin Park, Jae-Myung Lee, Hyo Sug Lee, Gerbrand Ceder
Chemistry of Materials, 2013, 25, 15, 3048–3055
Abstract
In this work, we investigated the effect of Rb and Ta doping on the ionic conductivity and stability of the garnet Li7+2x-y(La3-xRbx)(Zr2-yTay)O12 (0≤x≤0.375, 0≤y≤1) superionic conductor using first principles calculations. Our results indicate that doping does not greatly alter the topology of the migration pathway, but instead acts primarily to change the lithium concentration. The structure with the lowest activation energy and highest room temperature conductivity is Li6.75La3Zr1.75Ta0.25O12 (Ea = 19 meV, σ300K = 1 x 10-2 S cm-1). All Ta-doped structures have significantly higher ionic conductivity than the undoped cubic Li7La3Zr2O12 (c-LLZO, Ea = 24 meV, σ300K = 2 x 10-3 S cm-1). The Rb-doped structure with composition Li7.25La2.875Rb0.125Zr2O12, has a lower activation energy than c-LLZO, but further Rb doping leads to a dramatic decrease in performance. We also examined the effect of changing the lattice parameter at fixed lithium concentration and found that a decrease in the lattice parameter leads to a rapid decline in Li+ conductivity, whereas an expanded lattice offers only marginal improvement. This result suggests that doping with larger cations will not provide a significant enhancement in performance. Our results find higher conductivity and lower activation energy than is typically reported in the experimental literature, which suggests that there is room for improving the total conductivity in these promising materials.
Designing Multielectron Lithium-Ion Phosphate Cathodes by Mixing Transition Metals
Geoffroy Hautier, Anubhav Jain, Timothy Mueller, Charles Moore, Shyue Ping Ong, Gerbrand Ceder
Chemistry of Materials, 2013, 25, 10, 2064–2074
Abstract
Finding new polyanionic Li-ion battery cathodes with higher capacities than LiFePO4 is currently a major target of battery research. One approach towards this goal is to develop materials capable of exchanging more than one lithium per transition metal. However, constraints on operating voltage due to organic electrolyte stability as well as cathode structural stability have made this target difficult to reach. More specifically, it is very challenging to develop a phosphate-based cathode in which a single element provides +2 to +4 redox activity in a reasonable voltage window: either the +2/+3 couple is too low (e.g., V) or the +3/+4 couple is too high in voltage (e.g., Fe). This makes several appealing structural frameworks such as tavorites difficult to use as practical two-electron systems. Here, we propose a voltage design strategy based on the mixing of different transition metals in crystal structures known to be able to accommodate lithium in insertion and delithiation. By mixing a metal active on the +2/+3 couple (e.g., Fe) with an element active on the +3/+5 or +3/+6 couples (e.g., V or Mo), we show that high capacity multi-electron cathodes can be designed in an adequate voltage window. We illustrate our mixing strategy on LiMP2O7 pyrophosphates as well as LiMPO4(OH) and LiM(PO4)F tavorites, and we use density functional theory computations to evaluate the theoretical capacity, voltage profile and stability of the compounds proposed by our design rules. From this analysis, we identify several new compounds of potential interest as cathode materials.
First-principles study of iron oxyfluorides and lithiation of FeOF
Vincent L. Chevrier, Geoffroy Hautier, Shyue Ping Ong, Robert E. Doe, Gerbrand Ceder
Physical Review B, 2013, 87, 9, 094118
Abstract
First-principles studies of iron oxyfluorides in the FeF2 rutile framework (FeOxF2−x, 0 x 1) are performed using density functional theory (DFT) in the general gradient approximation (GGA) with a Hubbard U correction. Studies of O/F orderings reveal FeOF to be particularly stable compared to other FeOxF2−x (x = 1) structures, where FeF2-FeOF mixing is not energetically favored. The band gap of FeF2 is found to decrease as oxygen is substituted into its structure. The GGA + U electronic structure evolves from that of a Mott-Hubbard insulator (x = 0) to a charge transfer semiconductor (x = 1). Lithiation studies reveal that lithiation sites offering mixed O/F environments are the most stable. An insertion voltage plateau up to Li0.5FeOF on lithiation is found, in agreement with recent Li-ion battery experiments. The energetics of further lithiation with respect to conversion scenarios are discussed.
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent L. Chevrier, Kristin A. Persson, Gerbrand Ceder
Computational Materials Science, 2013, 68, 314–319
Abstract
We present the Python Materials Genomics (pymatgen) library, a robust, open-source Python library for materials analysis. A key enabler in high-throughput computational materials science efforts is a robust set of software tools to perform initial setup for the calculations (e.g., generation of structures and necessary input files) and post-calculation analysis to derive useful material properties from raw calculated data. The pymatgen library aims to meet these needs by (1) defining core Python objects for materials data representation, (2) providing a well-tested set of structure and thermodynamic analyses relevant to many applications, and (3) establishing an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calculations and experiments. The pymatgen library also provides convenient tools to obtain useful materials data via the Materials Project's REpresentational State Transfer (REST) Application Programming Interface (API). As an example, using pymatgen's interface to the Materials Project's RESTful API and phasediagram package, we demonstrate how the phase and electrochemical stability of a recently synthesized material, Li 4SnS4, can be analyzed using a minimum of computing resources. We find that Li4SnS4 is a stable phase in the Li-Sn-S phase diagram (consistent with the fact that it can be synthesized), but the narrow range of lithium chemical potentials for which it is predicted to be stable would suggest that it is not intrinsically stable against typical electrodes used in lithium-ion batteries. ?? 2012 Elsevier B.V. All rights reserved.
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson
APL Materials, 2013, 1, 1, 011002
Abstract
Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping” of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design.
Phase stability, electrochemical stability and ionic conductivity of the Li10±1MP2X12 (M = Ge, Si, Sn, Al or P, and X = O, S or Se) family of superionic conductors
Shyue Ping Ong, Yifei Mo, William Davidson Richards, Lincoln Miara, Hyo Sug Lee, Gerbrand Ceder
Energy & Environmental Science, 2013, 6, 1, 148
Abstract
We present an investigation of the phase stability, electrochemical stability and Li+ conductivity in the Li10MP2X12 (M = Ge, Si, Sn, Al or P, and X = O, S or Se) family of superionic conductors. The Li10GeP2S12 (LGPS) superionic conductor has the highest Li+ conductivity reported to date, with excellent electrochemical performance demonstrated in a Li-ion rechargeable battery. Our results show that isovalent cation substitutions of Ge4+ have a small effect on the relevant intrinsic properties, with Li10SiP2S12 and Li10SnP2S12 having similar phase stability, electrochemical stability and Li+ conductivity as LGPS. Aliovalent cation substitutions (M = Al or P) with compensating changes in Li+ concentration also have a small effect on the Li+ conductivity in this structure. Anion substitutions, however, have a much larger effect on these properties. The oxygen-substituted Li10MP2O12 compounds are in general predicted not to be stable (with equilibrium decomposition energies \textgreater 90 meV/atom) and have much lower Li+ conductivities than their sulphide counterparts. The selenium-substituted Li10MP2Se12 compounds, on the other hand, show a marginal improvement in conductivity, but at the expense of reduced electrochemical stability. We also studied the effect of lattice parameter changes on the Li+ conductivity and found the same asymmetry in behavior between increases and decreases in the lattice parameters, i.e., decreases in the lattice parameters lower the Li+ conductivity significantly, while increases in the lattice parameters increase the Li+ conductivity only marginally. Based on these results, we conclude that the size of the S2- is near optimal for Li+ conduction in this structural framework.
First Principles Study of the Li10GeP2S12 Lithium Super Ionic Conductor Material
Yifei Mo, Shyue Ping Ong, Gerbrand Ceder
Chemistry of Materials, 2012, 24, 1, 15–17
Abstract
In this paper, we investigate the diffusivity, stability, and electrochemical window of the Li10GeP2S12 (LGPS) lithium super ionic conductor using a variety of first principles techniques. LGPS was recently reported by Kamaya et al. as having the highest conductivity ever achieved among solid lithium electrolytes of 12 mS/cm at room temperature and outstanding electrochemical performance in Li batteries. We find that LGPS is a metastable phase in the calculated phase diagram and that LGPS is not stable against reduction by lithium at low voltage or extraction of Li with decomposition at high voltage. Our calculated lithium grand potential phase diagrams suggest that the observed wide electrochemical window of LGPS could be the result of the formation of passivation layers at the electrode-electrolyte that are nonetheless still high conducting and do not impede Li transport. We also identified the diffusion pathways and calculated the corresponding activation energies and diffusion coefficient using ab initio molecular dynamics simulations. Our simulations show that LGPS is in fact a three-dimensional ion conductor rather than a one-dimensional ion conductor. Our calculated overall activation barrier of 0.21 eV and conductivity of 9 mS/cm at room temperature are in remarkable agreement with the experimental results. In this paper, we investigate the diffusivity, stability, and electrochemical window of the Li10GeP2S12 (LGPS) lithium super ionic conductor using a variety of first principles techniques. LGPS was recently reported by Kamaya et al. as having the highest conductivity ever achieved among solid lithium electrolytes of 12 mS/cm at room temperature and outstanding electrochemical performance in Li batteries. We find that LGPS is a metastable phase in the calculated phase diagram and that LGPS is not stable against reduction by lithium at low voltage or extraction of Li with decomposition at high voltage. Our calculated lithium grand potential phase diagrams suggest that the observed wide electrochemical window of LGPS could be the result of the formation of passivation layers at the electrode-electrolyte that are nonetheless still high conducting and do not impede Li transport. We also identified the diffusion pathways and calculated the corresponding activation energies and diffusion coefficient using ab initio molecular dynamics simulations. Our simulations show that LGPS is in fact a three-dimensional ion conductor rather than a one-dimensional ion conductor. Our calculated overall activation barrier of 0.21 eV and conductivity of 9 mS/cm at room temperature are in remarkable agreement with the experimental results.
Community Accessible Datastore of High-Throughput Calculations: Experiences from the Materials Project
Dan Gunter, Shreyas Cholia, Anubhav Jain, Michael Kocher, Kristin Persson, Lavanya Ramakrishnan, Shyue Ping Ong, Gerbrand Ceder
2012 SC Companion: High Performance Computing, Networking Storage and Analysis, 2012, 1244–1251
Abstract
Efforts such as the Human Genome Project provided a dramatic example of opening scientific datasets to the community. Making high quality scientific data accessible through an online database allows scientists around the world to multiply the value of that data through scientific innovations. Similarly, the goal of the Materials Project is to calculate physical properties of all known inorganic materials and make this data freely available, with the goal of accelerating to invention of better materials. However, the complexity of scientific data, and the complexity of the simulations needed to generate and analyze it, pose challenges to current software ecosystem. In this paper, we describe the approach we used in the Materials Project to overcome these challenges and create and disseminate a high quality database of materials properties computed by solving the basic laws of physics. Our infrastructure requires a novel combination of highthroughput approaches with broadly applicable and scalable approaches to data storage and dissemination.
A comparison of destabilization mechanisms of the layered Na(x)MO2 and Li(x)MO2 compounds upon alkali de-intercalation.
Sangtae Kim, Xiaohua Ma, Shyue Ping Ong, Gerbrand Ceder
Physical chemistry chemical physics : PCCP, 2012, 14, 44, 15571–8
Abstract
To understand the difference in reversible energy storage capacity between the O3-type layered Na and Li compounds, we use first principles calculations to study and contrast the effect of two well-known destabilization mechanisms, transformation into the spinel-type structures and cation mixing due to transition metal migration. This study is performed on the layered oxides at the A(0.5)MO(2) composition, where A = (Na, Li) and M is a 3d transition metal. We find that while all Li(0.5)MO(2) compounds have strong driving forces and low energy kinetic paths to transform to the spinel structure, Na(0.5)MO(2) compounds do not have thermodynamic driving forces to transform to spinel type structures. We also find that transition metal mobility is higher in Li layered compounds than in Na layered compounds because of the unusual activated state for transition metal hopping. For many compounds, migration goes along an oct-tet-oct path, but transition metal migration needs to be assisted by alkali migration into a tetrahedral site forming activated A(tet)-M(tet) defects; substituting Na for Li in the layered structure results in increased transition metal migration barriers due to the larger size of Na(+) ions. Overall, our findings indicate that Na compounds in the layered O3 structure have fundamentally different destabilization mechanisms to those of Li compounds. This distinction allows superior battery electrode performance in many Na compounds and offers optimistic perspective on finding many high energy density Na electrodes that cycle with stable high capacity.
From the computer to the laboratory: materials discovery and design using first-principles calculations
Geoffroy Hautier, Anubhav Jain, Shyue Ping Ong
Journal of Materials Science, 2012, 47, 21, 7317–7340
Abstract
The development of new technological materials has historically been a difficult and time-consuming task. The traditional role of computation in materials design has been to better understand existing materials. However, an emerging paradigm for accelerated materials discovery is to design new compounds in silico using firstprinciples calculations, and then perform experiments on the computationally designed candidates. In this paper, we provide a review of ab initio computational materials design, focusing on instances in which a computational approach has been successfully applied to propose new materials of technological interest in the laboratory. Our examples include applications in renewable energy, electronic, magnetic and multiferroic materials, and catalysis, demonstrating that computationally guided materials design is a broadly applicable technique. We then discuss some of the common features and limitations of successful theoretical predictions across fields, examining the different ways in which first-principles calculations can guide the final experimental result. Finally, we present a future outlook in which we expect that new models of computational search, such as high-throughput studies, will play a greater role in guiding materials advancements.
Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability
Geoffroy Hautier, Shyue Ping Ong, Anubhav Jain, Charles J. Moore, Gerbrand Ceder
Physical Review B - Condensed Matter and Materials Physics, 2012, 85, 15, 155208
Abstract
The evaluation of reaction energies between solids using density functional theory (DFT) is of practical importance in many technological fields and paramount in the study of the phase stability of known and predicted compounds. In this work, we present a comparison between reaction energies provided by experiments and computed by DFT in the generalized gradient approximation (GGA), using a Hubbard U parameter for some transitionmetal elements (GGA+U).We use a data set of 135 reactions involving the formation of ternary oxides from binary oxides in a broad range of chemistries and crystal structures. We find that the computational errors can be modeled by a normal distribution with a mean close to zero and a standard deviation of 24 meV/atom. The significantly smaller error compared to the more commonly reported errors in the formation energies from the elements is related to the larger cancellation of errors in energies when reactions involve chemically similar compounds. This result is of importance for phase diagram computations for which the relevant reaction energies are often not from the elements but from chemically close phases (e.g., ternary oxides versus binary oxides). In addition, we discuss the distribution of computational errors among chemistries and show that the use of a Hubbard U parameter is critical to the accuracy of reaction energies involving transition metals even when no major change in formal oxidation state is occurring.
Low hole polaron migration barrier in lithium peroxide
Shyue Ping Ong, Yifei Mo, Gerbrand Ceder
Physical Review B, 2012, 85, 8, 2–5
Abstract
We present computational evidence of polaronic hole trapping and migration in lithium peroxide (Li2O2), a material of interest in lithium-air batteries.We find that the hole forms in the π ∗ antibonding molecular orbitals of the peroxide (O2− 2 ) anion, and that this trapped hole induces significant local lattice distortion, forming a polaron. Our study finds migration barriers for the free polaron to be between 68 and 152 meV, depending on the hopping direction. This low barrier suggests that this material might not be as insulating as previously assumed, provided that the formation of carriers can be achieved. One transport limitation may arise from lithium vacancies, which we find to strongly bind to the polaron. This result, in combination with previous experimental results, suggests that electronic conductivity in this material is likely to be determined by vacancy diffusion.
Recharging lithium battery research with first-principles methods
Gerbrand Ceder, Geoffroy Hautier, Anubhav Jain, Shyue Ping Ong
MRS Bulletin, 2012, 37, 02, b1–b2
Abstract
Energy storage is a critical hurdle to the success of many clean energy technologies. Batteries with high energy density, good safety, and low cost can enable more effi cient vehicles with electrifi ed drive trains, such as hybrid electric vehicles, electric vehicles, and plug-in hybrid electric vehicles. They can also provide energy storage for intermittent energy sources, such as wind and solar. Today, and for the foreseeable future, rechargeable lithium batteries deliver the highest energy per unit weight or volume at reasonable cost. Many of the important properties of battery materials can be calculated with fi rst-principles methods, making lithium batteries fertile ground for computational materials design. In this article, we review the successes and opportunities in using fi rst-principles computations in the battery fi eld. We also highlight some technical challenges facing the accurate modeling of battery materials.
First-principles insights on the magnetism of cubic SrTi1−xCoxO3−δ
Juan M Florez, Shyue Ping Ong, M. C. Onbaşli, Gerald F Dionne, P Vargas, Gerbrand Ceder, Caroline A Ross
Applied Physics Letters, 2012, 100, 25, 252904
Abstract
We present hybrid density functional calculations suggesting that magnetism in cubic SrTi1−xCoxO3−δ (STCO) with x = 0.25 is sensitive to the nearest neighbor arrangement of the Co and the presence of oxygen vacancies. Spin polarized calculations for x = 0.25 in which the nearest neighbor (nn) Co spacing is a, a or a with a the lattice parameter predict lowest energies for the a nn separation and favor the ferromagnetic state. Oxygen deficiency (δ = 0.125) lowers the average Co valence state and favors mixed valence and spin states (high spin for the Co adjacent to the vacancy and low for the non-adjacent Co), an increase of the band gap and an expansion of the lattice parameter compared to stoichiometric STCO in which both Co ions are low spin. Predicted configurations of the two neighboring Co ions are (t2g5eg0, t2g5eg0) and (t2g4eg2, t2g6eg0) with average 1.0 and 1.6 μB/Co for stoichiometric and 1-O-vacancy systems, respectively.
First-principles study of the oxygen evolution reaction of lithium peroxide in the lithium-air battery
Yifei Mo, Shyue Ping Ong, Gerbrand Ceder
Physical Review B, 2011, 84, 20, 205446
Abstract
The lithium-air chemistry is an interesting candidate for the next-generation batteries with high specific energy. However, this new battery technology is facing substantial challenges, such as a high overpotential upon charging, poor reversibility, and low power density. Using first-principles calculations, we study the oxygen evolution reaction (OER) on the low-index surfaces of lithium peroxide. The elementary reaction steps and the energy profile of the OER are identified on the low-index surfaces of lithium peroxide. We find that the OER processes are kinetically limited by the high energy barrier for the evolution of oxygen molecules and that the rate of the OER processes is highly dependent on the surface orientation.
Voltage, stability and diffusion barrier differences between sodium-ion and lithium-ion intercalation materials
Shyue Ping Ong, Vincent L. Chevrier, Geoffroy Hautier, Anubhav Jain, Charles Moore, Sangtae Kim, Xiaohua Ma, Gerbrand Ceder
Energy & Environmental Science, 2011, 4, 9, 3680–3688
Abstract
To evaluate the potential of Na-ion batteries, we contrast in this work the difference between Na-ion and Li-ion based intercalation chemistries in terms of three key battery properties—voltage, phase stability and diffusion barriers. The compounds investigated comprise the layered AMO2 and AMS2 structures, the olivine and maricite AMPO4 structures, and the NASICON A3V2(PO4)3 structures. The calculated Na voltages for the compounds investigated are 0.18–0.57 V lower than that of the corresponding Li voltages, in agreement with previous experimental data. We believe the observed lower voltages for Na compounds are predominantly a cathodic effect related to the much smaller energy gain from inserting Na into the host structure compared to inserting Li. We also found a relatively strong dependence of battery properties on structural features. In general, the difference between the Na and Li voltage of the same structure, DVNa–Li, is less negative for the maricite structures preferred by Na, and more negative for the olivine structures preferred by Li. The layered compounds have the most negative DVNa–Li. In terms of phase stability, we found that open structures, such as the layered and NASICON structures, that are better able to accommodate the larger Na+ ion generally have both Na and Li versions of the same compound. For the close-packed AMPO4 structures, our results show that Na generally prefers the maricite structure, while Li prefers the olivine structure, in agreement with previous experimental work. We also found surprising evidence that the barriers for Na+ migration can potentially be lower than that for Li+ migration in the layered structures. Overall, our findings indicate that Na-ion systems can be competitive with Li-ion systems.
Phosphates as Lithium-Ion Battery Cathodes: An Evaluation Based on High-Throughput ab Initio Calculations
Geoffroy Hautier, Anubhav Jain, Shyue Ping Ong, ByoungWoo Kang, Charles Moore, Robert Doe, Gerbrand Ceder
Chemistry of Materials, 2011, 23, 15, 3495–3508
Abstract
Phosphate materials are being extensively studied as lithium-ion battery electrodes. In this work, we present a highthroughput ab initio analysis of phosphates as cathode materials. Capacity, voltage, specific energy, energy density, and thermal stability are evaluated computationally on thousands of compounds. The limits in terms of gravimetric and volumetric capacity inherent to the phosphate chemistry are determined. Voltage ranges for all redox couples in phosphates are provided, and the structural factors influencing the voltages are analyzed. We reinvestigate whether phosphate materials are inherently safe and find that, for the same oxidation state, oxygen release happens thermodynamically at lower temperature for phosphates than for oxides. These findings are used to recommend specific chemistries within the phosphate class and to show the intrinsic limits of certain materials of current interest (e.g., LiCoPO4 and LiNiPO4).
Formation enthalpies by mixing GGA and GGA+U calculations
Anubhav Jain, Geoffroy Hautier, Shyue Ping Ong, Charles J. Moore, Christopher C. Fischer, Kristin A. Persson, Gerbrand Ceder
Physical Review B, 2011, 84, 4, 045115
Abstract
Standard approximations to the density functional theory exchange-correlation functional have been extraordinary successful, but calculating formation enthalpies of reactions involving compounds with both localized and delocalized electronic states remains challenging. In this work we examine the shortcomings of the generalized gradient approximation (GGA) and GGA+U in accurately characterizing such difficult reactions. We then outline a methodology that mixes GGA and GGA+U total energies (using known binary formation data for calibration) to more accurately predict formation enthalpies. We demonstrate that for a test set of 49 ternary oxides, our methodology can reduce the mean absolute relative error in calculated formation enthalpies from approximately 7.7–21% in GGA+U to under 2%. As another example we show that neither GGA nor GGA+U alone accurately reproduces the Fe-P-O phase diagram; however, our mixed methodology successfully predicts all known phases as stable by naturally stitching together GGA and GGA+U results. As a final example we demonstrate how our technique can be applied to the calculation of the Li-conversion voltage of LiFeF3. Our results indicate that mixing energies of several functionals represents one avenue to improve the accuracy of total energy computations without affecting the cost of calculation.
Electrochemical Windows of Room-Temperature Ionic Liquids from Molecular Dynamics and Density Functional Theory Calculations
Shyue Ping Ong, Oliviero Andreussi, Yabi Wu, Nicola Marzari, Gerbrand Ceder
Chemistry of Materials, 2011, 23, 11, 2979–2986
Abstract
We investigated the cathodic and anodic limits of six room-temperature ionic liquids (ILs) formed from a combination of two common cations, 1-butyl-3-methylimidazolium (BMIM) and N,N-propylmethylpyrrolidinium (P13), and three common anions, PF6, BF4, and bis(trifluoromethylsulfonyl)imide (TFSI), using an approach that combines molecular dynamics (MD) simulations and density functional theory (DFT) calculations. All interion interactions were taken into account by explicitly modeling the entire liquid structure using classical MD, followed by DFT computations of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies. The relative cathodic and anodic limits of BMIM PF6, BMIM BF4, BMIM TFSI, and P13 TFSI obtained from our approach are in fairly good agreement with existing experimental data. From our DFT calculations, we also obtained the cation- and anion-projected density of states (DOS), which provide information on the likely species contributing to reductive and oxidative decomposition. Our predictions support Howlett et al.'s earlier finding1 that the TFSI anion is less stable than the P13 cation against reduction. In addition, our results provide surprising evidence of possible cation anodic instability; we predict the aromatic BMIM cation to be less stable against oxidation than the respective anions in BMIM PF6 and BMIM BF4, and the P13 cation to be less stable against oxidation than the PF6 anion in P13 PF6. We also present a comparison of the predictions of our approach with that of simpler approximations based on in vacuo or polarizable continuum model calculations.
A high-throughput infrastructure for density functional theory calculations
Anubhav Jain, Geoffroy Hautier, Charles J. Moore, Shyue Ping Ong, Christopher C Fischer, Tim Mueller, Kristin A Persson, Gerbrand Ceder
Computational Materials Science, 2011, 50, 8, 2295–2310
Abstract
The use of high-throughput density functional theory (DFT) calculations to screen for new materials and conduct fundamental research presents an exciting opportunity for materials science and materials innovation. High-throughput DFT typically involves computations on hundreds, thousands, or tens of thousands of compounds, and such a change of scale requires new calculation and data management methodologies. In this article, we describe aspects of the necessary data infrastructure for such projects to handle data generation and data analysis in a scalable way. We discuss the problem of accurately computing properties of compounds across diverse chemical spaces with a single exchange correlation functional, and demonstrate that errors in the generalized gradient approximation are highly dependent on chemical environment.
Comparison of small polaron migration and phase separation in olivine LiMnPO4 and LiFePO4 using hybrid density functional theory
Shyue Ping Ong, Vincent L. Chevrier, Gerbrand Ceder
Physical Review B, 2011, 83, 7, 075112
Abstract
Using hybrid density functional theory based on the Heyd-Scuseria-Ernzerhof (HSE06) functional, we compared polaron migration and phase separation in olivine LiMnPO4 to LiFePO4. The barriers for free hole and electron polaron migration in the Mn olivine system are calculated to be 303 and 196 meV, respectively, significantly higher than the corresponding barriers of 170 and 133 meV, respectively, for the Fe olivine system, in agreement with previous experimental findings. These results suggest that the electronic conductivities of LiMnPO4 and MnPO4 are about 177 and 11 times lower than their respective Fe analogs at room temperature. In the presence of lithium vacancies or ions, the barriers for both hole and electron polaron migration were found to be about 100–120 meV higher in the Mn olivine. The HSE06 functional, with its more universal treatment of self-interaction error, was found to be essential to the proper localization of a polaron in the Mn olivine but predicted qualitatively incorrect phase separation behavior in the LixFePO4 system.
Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations
Geoffroy Hautier, Anubhav Jain, Hailong Chen, Charles Moore, Shyue Ping Ong, Gerbrand Ceder
Journal of Materials Chemistry, 2011, 21, 17147–17153
Abstract
The discovery of new chemistries outperforming current lithium intercalation cathodes is of major technological importance. In this context, polyanionic systems with the potential to exchange multiple electrons per transition metal are particularly interesting because they could combine the safety of polyanion systems with higher specific energy. In this paper, we report on a series of new mixed polyanions compounds of formula AxM(YO3)(XO4) (with A ¼ Na, Li; X ¼ Si, As, P; Y ¼ C, B; M ¼ a redox active metal; and x ¼ 0 to 3) identified by high-throughput ab initio computing. The computed stability of both lithium and sodium-based compounds is analyzed along with the voltage, specific energy and energy density of the lithium-based compounds. This analysis suggests several novel carbonophosphates and carbonosilicates as potential high capacity (\textgreater200 mAh/g) and specific energy (\textgreater700 Wh/kg) cathode materials for lithium-ion batteries.
Hybrid density functional calculations of redox potentials and formation energies of transition metal compounds
Vincent L. Chevrier, Shyue Ping Ong, Rickard Armiento, Maria K. Y. Chan, Gerbrand Ceder
Physical Review B, 2010, 82, 7, 075122
Abstract
We compare the accuracy of conventional semilocal density functional theory (DFT), the DFT+U method, and the Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional for structural parameters, redox reaction energies, and formation energies of transition metal compounds. Conventional DFT functionals significantly underestimate redox potentials for these compounds. Zhou et al. [Phys. Rev. B 70, 235121 (2004)] addressed this issue with DFT+U and a linear-response scheme for calculating U values. We show that the Li intercalation potentials of prominent Li-ion intercalation battery materials, such as the layered LixMO2 (M=Co and Ni), LixTiS2; olivine LixMPO4 (M=Mn, Fe, Co, and Ni); and spinel-like LixMn2O4, LixTi2O4, are also well reproduced by HSE06, due to the self-interaction error correction from the partial inclusion of Hartree-Fock exchange. For formation energies, HSE06 performs well for transition metal compounds, which typically are not well reproduced by conventional DFT functionals but does not significantly improve the results of nontransition metal oxides. Hence, we find that hybrid functionals provide a good alternative to DFT+U for transition metal applications when the large extra computational effort is compensated by the benefits of (i) avoiding species-specific adjustable parameters and (ii) a more universal treatment of the self-interaction error that is not exclusive to specific atomic orbital projections on selected ions.
Thermal stabilities of delithiated olivine MPO4 (M=Fe, Mn) cathodes investigated using first principles calculations
Shyue Ping Ong, Anubhav Jain, Geoffroy Hautier, Byoungwoo Kang, Gerbrand Ceder
Electrochemistry Communications, 2010, 12, 3, 427–430
Abstract
We present an analysis of the thermal reduction of delithiated LiMnPO4 and LiFePO4 based on the quarternary phase diagrams as calculated from first principles. Our results confirm the recent experimental findings that MnPO4 decomposes at a much lower temperature than FePO4, thereby potentially posing larger safety issues for LiMnPO4 cathodes. We find that while substantial oxygen is released as MnPO4 reduces to Mn2P2O7, the mixed valence phases that form in the decomposition process of FePO4 limit the amount of oxygen evolved.
Investigation of the Effect of Functional Group Substitutions on the Gas-Phase Electron Affinities and Ionization Energies of Room-Temperature Ionic Liquids Ions using Density Functional Theory
Shyue Ping Ong, Gerbrand Ceder
Electrochimica Acta, 2010, 55, 11, 3804–3811
Abstract
The cathodic and anodic stabilities of room-temperature ionic liquids (ILs) are important factors in their applications in electrochemical devices. In this work, we investigated the electron affinities of cations and ionization energies of anions for ionic liquids by density functional theory (DFT) calculations at the B3LYP/6-311+G(2d,p)//B3LYP/6-31+G(d) level. Over 200 unique cations and anions, formed from a set of six base cation structures, three base anion structures, and seven functional groups, were investigated.We find the trends in calculated EAs of alkylated cations and IEs of alkylated anions to be in good agreement with observed experimental trends in relative cathodic and anodic stabilities of various ILs. In addition, we also investigated the effect that functional group substitution at distinct positions in the ions have on the EA of the 1,2,3-trimethylimidazolium cation and the IE of the PF5CF3 anion. The overall impact on the EA or IE can be explained by the known electron-donating and electron-withdrawing inductive and resonance effects of the attached functional group, and the relative strength of the effect depends on the substitution position.
Li−Fe−P−O2 Phase Diagram from First Principles Calculations
Shyue Ping Ong, Lei Wang, Byoungwoo Kang, Gerbrand Ceder
Chemistry of Materials, 2008, 20, 5, 1798–1807
Abstract
We present an efficient way to calculate the phase diagram of the quaternary Li−Fe−P−O2 system using ab initio methods. The ground-state energies of all known compounds in the Li−Fe−P−O2 system were calculated using the generalized gradient approximation (GGA) approximation to density functional theory (DFT) and the DFT+U extension to it. Considering only the entropy of gaseous phases, the phase diagram was constructed as a function of oxidation conditions, with the oxygen chemical potential, μO2, capturing both temperature and oxygen partial pressure dependence. A modified Ellingham diagram was also developed by incorporating the experimental entropy data of gaseous phases. The phase diagram shows LiFePO4 to be stable over a wide range of oxidation environments, being the first Fe2+-containing phase to appear upon reduction at μO2 = −11.52 eV and the last of the Fe-containing phosphates to be reduced at μO2 = −16.74 eV. Lower μO2 represents more reducing conditions, which generally correspond to higher t...
bottom of page