Machine-learning potentials for crystal defects
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43579_2022_Article_221.pdf
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3.48 MB
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Author(s)
Freitas, Rodrigo
Cao, Yifan
Date Issued
August 12, 2022
Publisher
Springer International Publishing
Citation
Freitas, Rodrigo and Cao, Yifan. 2022. "Machine-learning potentials for crystal defects."
Version
Final published version
Abstract
Abstract
Decades of advancements in strategies for the calculation of atomic interactions have culminated in a class of methods known as machine-learning interatomic potentials (MLIAPs). MLIAPs dramatically widen the spectrum of materials systems that can be simulated with high physical fidelity, including their microstructural evolution and kinetics. This framework, in conjunction with cross-scale simulations and in silico microscopy, is poised to bring a paradigm shift to the field of atomistic simulations of materials. In this prospective article we summarize recent progress in the application of MLIAPs to crystal defects.
Graphical abstract
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1557/s43579-022-00221-5
