Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes

Bingning Wang, Hieu A. Doan, Seoung-Bum Son, Daniel P. Abraham, Stephen E. Trask, Andrew Jansen, Kang Xu () and Chen Liao ()
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Bingning Wang: Argonne National Laboratory
Hieu A. Doan: Argonne National Laboratory
Seoung-Bum Son: Argonne National Laboratory
Daniel P. Abraham: Argonne National Laboratory
Stephen E. Trask: Argonne National Laboratory
Andrew Jansen: Argonne National Laboratory
Kang Xu: SES AI Corps
Chen Liao: Argonne National Laboratory

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract LiNi0.5Mn1.5O4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6–4.7 V vs Li+/Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments.

Date: 2025
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DOI: 10.1038/s41467-025-57961-w

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