Author

Kristin A. Persson

3 papers indexed here

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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 Aug 2026

An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space

As the number of theoretically predicted materials continues to expand, it is increasingly important to evaluate not only their thermodynamic stability but also their kinetic resilience against transformation to competing polymorphs. In this study, we investigate the hypothesis that the kinetic persistence of a metasta...

Max C. Gallant, David Mrdjenovich, Kristin A. Persson · Journal of Chemical Theory a... · 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