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

Wang, Bingning; Doan, Hieu A.; Son, Seoung-Bum; Abraham, Daniel P.; Trask, Stephen E.; Jansen, Andrew; Xu, Kang; Liao, Chen

doi:10.1038/s41467-025-57961-w

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

Journal Article · Thu Apr 10 00:00:00 EDT 2025 · Nature Communications

DOI:https://doi.org/10.1038/s41467-025-57961-w· OSTI ID:2554071

Wang, Bingning; Doan, Hieu A.; ; ; ; ; ;

Abstract

LiNi _0.5 Mn _1.5 O ₄ (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.

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Sponsoring Organization:: USDOE

OSTI ID:: 2554071

Alternate ID(s):: OSTI ID: 2583989

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 16; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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

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

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