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

作者：Bingning Wang, Hieu Anh Doan, Seoung‐Bum Son, Daniel P. Abraham, Stephen E. Trask, Andrew N. Jansen, Kang Xu, Chen Liao · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-57961-w · 被引用次数：48 · 研究领域：Advancements in Battery Materials、Advanced Battery Technologies Research、Advanced Battery Materials and Technologies

/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.