Distilled compact MLIPs from transfer-learned teachers reproduce observables more reliably than same-size models trained directly and enable practical PIMD umbrella sampling of water dissociation at TiO2 interface with NQE effects matching NMR.
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Presents a path-integral molecular dynamics implementation of quantum annealing for global optimization of atomic structures using empirical or machine-learned potentials.
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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry
Distilled compact MLIPs from transfer-learned teachers reproduce observables more reliably than same-size models trained directly and enable practical PIMD umbrella sampling of water dissociation at TiO2 interface with NQE effects matching NMR.
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Quantum annealing for materials
Presents a path-integral molecular dynamics implementation of quantum annealing for global optimization of atomic structures using empirical or machine-learned potentials.