DFT survey of H, He, B, C, N, O, P, S at six CSL grain boundaries in ferritic iron finds B and C strengthen cohesion while He, O, S act as powerful decohesives, with public data release.
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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.
citing papers explorer
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Grain boundary segregation of light elements and their effects on cohesion in ferritic steels
DFT survey of H, He, B, C, N, O, P, S at six CSL grain boundaries in ferritic iron finds B and C strengthen cohesion while He, O, S act as powerful decohesives, with public data release.
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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.