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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.
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