758 citations · 2.5k across the 23 of their papers we have counts for
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cond-mat.mtrl-sci2021★ 392 cited
Machine-learning interatomic potentials for materials science
Y. Mishin
Large-scale atomistic computer simulations of materials rely on interatomic potentials providing computationally efficient predictions of energy and Newtonian forces. Traditional p…
cond-mat.mtrl-sci2021★ 31 cited
Development of a physically-informed neural network interatomic potential for tantalum
Yi-Shen Lin, Ganga P. Purja Pun, Yuri Mishin
Large-scale atomistic simulations of materials heavily rely on interatomic potentials, which predict the system energy and atomic forces. One of the recent developments in the fiel…