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Recent Advances and Outstanding Challenges for Machine Learning Interatomic Potentials

Tsz Wai Ko; Shyue Ping Ong

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.

National University of Singapore
College of Design and Engineering
Department of Materials Science and Engineering
9 Engineering Drive 1, Blk EA, #03-09
Singapore 117575
Singapore 

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