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Recent advances and outstanding challenges for machine learning interatomic potentials

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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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Fig. 1: Types of machine learning interatomic potentials.

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Acknowledgements

This work was primarily supported by the Materials Project, funded by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no. DE-AC02-05-CH11231: Materials Project program KC23MP. T.W.K also acknowledges support from the Schmidt AI in Science Postdoctoral Fellowship.

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  1. Department of NanoEngineering, University of California San Diego, La Jolla, CA, USA

    Tsz Wai Ko & Shyue Ping Ong

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  1. Tsz Wai Ko
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  2. Shyue Ping Ong
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Correspondence to Shyue Ping Ong.

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Ko, T.W., Ong, S.P. Recent advances and outstanding challenges for machine learning interatomic potentials. Nat Comput Sci 3, 998–1000 (2023). https://doi.org/10.1038/s43588-023-00561-9

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