Convergence Acceleration in Machine Learning Potentials for Atomistic Simulations

Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing accuracy...

Materials Science Computer Science Physics

Paper

References (4)

01SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic PotentialsSimon L. Batzner, T. Smidt, Lixin Sun et al.2021 · arXiv.org · 80 citations

02E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentialsSimon L. Batzner, Albert Musaelian, Lixin Sun et al.2021 · Nature Communications · 2.2k citations In Library

03Data Availability Statement: Convergence Acceleration in Machine Learning Potentials for Atomistic SimulationsDylan J. Bayerl, Christopher M. Andolina, S. Dwaraknath et al.2022 · 6 citations

04SE(3)-Equivariant Graph Neural Networks for Data-Ecient and Accurate Interatomic PotentialsSimon L. Batzner, T. Smidt, Lixin Sun et al.82 citations

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