Author

Bryant Y. Li

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Sep 2026

Machine Learning Interatomic Potentials for Modeling Solid-State Batteries

Atomistic simulations can provide critical insights into the fundamental behavior of battery materials. A necessary input for such simulations is a description of the potential energy surface (PES). Over the past decade, machine learning interatomic potentials (MLIPs) have emerged as a powerful approach to model the...

Mgcini Keith Phuthi, Grace Wei, Bryant Y. Li et al. · Chemistry of Materials · 0 citations

Open access Sep 2026

Distinguishing the mixed conduction interphase: a machine learning molecular dynamics study on the Na3PS4/Na battery interface

The interphase formed between solid electrolytes and alkali metal anodes determines whether decomposition is self-passivating, solid electrolyte interphase (SEI) or continuously propagating, mixed conducting interphase (MCI). We employ machine learning interatomic potential molecular dynamics to simulate the Na 3PS 4/N...

Bryant Y. Li, Hwidong Jeon, Kristin A. Persson · Machine Learning: Science an... · 0 citations