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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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