Conditional generative models double the rate of stable novel MAX phase structures by steering generation with MXene derivative counts and A-site binding energy surrogates, yielding five DFT-stable candidates out of ten tested.
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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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Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space
Conditional generative models double the rate of stable novel MAX phase structures by steering generation with MXene derivative counts and A-site binding energy surrogates, yielding five DFT-stable candidates out of ten tested.
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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.