GRACE MLIPs train faster and predict alloy properties more accurately than NEP, but NEP's 60-fold speed advantage enables reliable million-atom simulations of shock propagation when paired with ensemble uncertainty quantification.
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Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys
GRACE MLIPs train faster and predict alloy properties more accurately than NEP, but NEP's 60-fold speed advantage enables reliable million-atom simulations of shock propagation when paired with ensemble uncertainty quantification.