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At Materialyze.AI Lab, we are on a mission to pioneer the integration of theory, experiments, and AI to accelerate the discovery and deployment of breakthrough materials. We welcome postdoctoral and PhD applicants. We believe that the right team can make all the difference. Our Core Values At Materialyze.AI Lab, our core values guide everything we do. Here are the key principles we stand by: Integrity : We practice integrity in all forms. We are honest and fair to fellow group members and collaborators. We have a zero-tolerance policy towards plagiarism and falsification of results. Excellence: We strive for excellence in everything that we do. We stand by the quality of our science. We aim to develop scientists with great analytical, technical and communication skills. Teamwork: We believe great teamwork is the key to great science. We share and discuss ideas freely. We strive to build great collaborations, both within and outside of the group. We contribute actively to the materials science community. Opportunities at Materialyze.AI Lab We welcome postdoctoral and PhD applicants with expertise in either theory & AI or experimental materials research, or ideally, a combination of both. Theory & AI in Materials Discovery Develop and apply machine learning and AI models (e.g., ML interatomic potentials, generative design, reinforcement learning) to predict and design novel materials. Perform first-principles and molecular dynamics simulations to model structural, thermodynamic, and electronic properties. Contribute to open-source software, benchmarks, and datasets that advance the global materials community. Experimental Materials & AI Integration Synthesize and process functional materials relevant to batteries, aerospace alloys, and semiconductors using solid-state, solution, or thin-film methods. Apply advanced characterization techniques (XRD, TEM, SEM, spectroscopy, electrochemistry, etc.) to probe structure–property relationships. Collaborate with theory and AI researchers to validate predictions, generate datasets, and develop high-throughput/automated experimental workflows. Experience in developing autonomous laboratory systems is a strong plus. Application Process Submit Your Application : Start by submitting a cover letter, CV, and 3+ referee contacts via this form . Initial Review : This usually takes place within two weeks of your submission. Initial interview via Zoom: We invite a subset of applicants for an initial interview with Prof Ong and his postdoctoral associates. In this initial interview, you will be requested to prepare a short 10-slide/10-min presentation summarizing your research experience, what you hope to get out of your time and what you feel you would be able to bring to our lab. You will be informed of the results of this initial interview within a week. Full interview for postdoctoral applicants: We invite you for a 2-hour interview with the whole group. You will be requested to give an hour-long presentation that outlines your research accomplishments and future plans in greater detail. Conditions permitting, this interview will take place in person and all travel expenses will be covered. In the event this is not possible, the interview will take place via Zoom. This interview is also an opportunity for you to get to know the group members and ask any questions you may have. Admission application for PhD candidates: You will be asked to submit an application for admission to the Doctor of Engineering (Materials Science and Engineering) program via the NUS application portal . In your application, please indicate in the section asking about your research interest and potential supervisor that you would like to work with Professor Shyue Ping Ong. Offer : If everything goes well, we will extend an offer to join our team. We will provide you with all the details regarding salary, benefits, and start date. What to Expect as a Team Member Once you join Materialyze.AI Lab, you will be welcomed into a supportive and engaging environment. Here are some things you can expect: Onboarding : We provide a comprehensive onboarding process to help you get settled. You will receive training and resources to ensure you are set up for success. Mentorship : You will have access to mentors who can guide you. They will provide support and advice as you navigate your new role. Continuous Learning : We believe in lifelong learning. You will have opportunities to attend workshops and conferences to enhance your skills. Team Building Activities : We organize regular team-building events to foster camaraderie. These activities help strengthen relationships and create a positive work culture. Join Us Today If you are excited about the prospect of working at Materialyze.AI Lab, we encourage you to apply. We are looking for individuals who are passionate, driven, and ready to make a difference.

Foundation potentials (FPs), i.e., universal machine learning interatomic potentials with periodic-table-wide coverage, are now proliferating. New pre-trained FPs seem to appear almost daily, making hyped-up claims about performance. For those not deep in the field, it’s becoming increasingly difficult to separate signal from noise. Here’s my expert, very opinionated take on how to choose an FP that actually fits your needs. 1️⃣ Dataset quality and size drive everything. Architecture is a secondary consideration. If you’re working with inorganic crystals or condensed matter, there are really only two datasets that matter today: OMat24 and MatPES (yes, I’m involved with the latter, but I’m being objective here). OMat24 wins on sheer size and broad coverage near equilibrium, but its convergence criteria based on MPRelax parameters are a bit loose for true PES quality, i.e., the kind that people use to train plain MLIPs. But at least OMat24 is constructed from static calculations, not merely sampled from intermediate steps in a relaxation trajectory or MD, such as MPTrj. MatPES is smaller, but its parameters are modernized for PES accuracy - the latest pseudopotentials, strict energy and force convergence, etc. Our experience is that the predictions are much more reliable, and it is much easier to work with. It is the only option if you want r2SCAN PES information, which for many systems, is far superior to PBE. Pick either one. The community should retire MPTrj — it was never meant for MLIP training and was just the best available dataset in the early days of FPs. 2️⃣ Don’t mix datasets unless you really know what you’re doing. I’m still amazed how often people blend OMat24, Alexandria, and MPTrj thinking it improves generality. It doesn’t. Mixing a high-quality dataset with a low-quality one yields a low-quality model. 3️⃣ Choose architecture efficiency over novelty. Unless you’re after new physics beyond PES quantities (like charges or magnetism), focus on architectures that give the best accuracy with the fewest parameters and lowest inference cost. At this point, the major FP architectures perform similarly in terms of accuracy. The real differentiator is computational efficiency. Some of the more models are more than 1000× slower  at inference and can barely handle systems beyond 100 atoms. This defeats the entire purpose of a FP: being a practical surrogate for DFT . If you just want to play around with different FPs to check their performance on a property that matters to you, MatCalc is a good choice to get this done with a minimal amount of coding.