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physics.chem-ph2025
Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells
Kamron Fazel, Sam Brown, Jacob Clary +5
Machine-learned interatomic potentials (MLIPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLIPs in multicomponent sys…
physics.chem-ph2024★ 17 cited
Bridging electronic and classical density-functional theory using universal machine-learned functional approximations
Michelle M. Kelley, Joshua Quinton, Kamron Fazel +3
The accuracy of density-functional theory (DFT) is determined by the quality of the approximate functionals, such as exchange-correlation in electronic DFT and the excess functiona…