17 citations · 17 across the 3 of their papers we have counts for
4 papers
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…
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…
First-principles molten salt phase diagrams through thermodynamic integration
Tanooj Shah, Kamron Fazel, Jie Lian +3
Precise prediction of phase diagrams in molecular dynamics (MD) simulations is challenging due to the simultaneous need for long time scales, large length scales and accurate inter…
Improving the reliability of machine learned potentials for modeling inhomogenous liquids
Kamron Fazel, Nima Karimitari, Tanooj Shah +2
The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical and biological process…