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Machine learning for interatomic potential models

Tim Mueller; Alberto Hernández; Chuhong Wang

The Journal of Chemical Physics · 2020 · Inggris · OpenAlex Scopus

The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal…