Machine Learning Interatomic Potentials for Modeling Solid-State Batteries

Abstract 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 PES at near-first-principles accuracy with orders of magnitude lower computational cost. In this perspective, we examine the role of atomistic simulation in battery research, especially in the context of the capabilities afforded by MLIPs. We outline their advantages and trade-offs relative to ab initio methods and discuss how they enable the prediction of key intrinsic properties relevant to battery materials, especially for solid state batteries. We further explore their potential for modeling interfaces and chemical reactivity, as well as their integration into high-throughput screening and materials discovery workflows. Finally, we highlight promising applications and identify requirements for the next generation of MLIPs to advance the study of battery materials.

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Publication Details

Journal
Chemistry of Materials
Published
2026-09-28
DOI
https://doi.org/10.1021/acs.chemmater.6c01051
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Machine Learning Interatomic Potentials for Modeling Solid-State Batteries

Shyue Ping Ong, Gerbrand Ceder, Sauradeep Majumdar, Rafael Gómez‐Bombarelli et al.
Chemistry of Materials
Machine Learning in Materials Science
article

Machine Learning Interatomic Potentials for Modeling Solid-State Batteries

Shyue Ping Ong, Gerbrand Ceder, Sauradeep Majumdar, Rafael Gómez‐Bombarelli, Bryant Y. Li, Samuel M. Blau, Maria K. Y. Chan, Pieremanuele Canepa, Nitesh Kumar, Venkata Surya Chaitanya Kolluru, Mgcini Keith Phuthi, Grace Wei, Kristin Persson
article en

Abstract

Abstract 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 PES at near-first-principles accuracy with orders of magnitude lower computational cost. In this perspective, we examine the role of atomistic simulation in battery research, especially in the context of the capabilities afforded by MLIPs. We outline their advantages and trade-offs relative to ab initio methods and discuss how they enable the prediction of key intrinsic properties relevant to battery materials, especially for solid state batteries. We further explore their potential for modeling interfaces and chemical reactivity, as well as their integration into high-throughput screening and materials discovery workflows. Finally, we highlight promising applications and identify requirements for the next generation of MLIPs to advance the study of battery materials.

Chemistry of Materials
Argonne National Laboratory (US), Lawrence Berkeley National Laboratory (US), National University of Singapore (SG), University of California San Diego (US), University of Houston (US), The Ohio State University (US), Massachusetts Institute of Technology (US), University of California, Berkeley (US)
Affordable and clean energy
Openalex Percentile: Top 26%
Machine Learning in Materials Science
DOISourceOpenAlex
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