Screening “Cathode-Stable and Anode-Reactive” Sulfur-Based Additives for LiNi_0.5Mn_1.5O₄||Graphite Pouch Cells via a Machine Learning Approach

To address this challenge, we present a machine learning (ML)-based approach with a gradient boosting regression (GBR) model to predict the highest occupied molecular orbital (HOMO) energy levels, which identifies the SO group as a key structural element influencing additive effectiveness.

Subsequently, by integrating density functional theory (DFT) calculations with molecular descriptor analysis, BDTT is identified as a promising candidate. Electrochemical evaluations demonstrate that, at 4.85 V and 45 °C, the incorporation of BDTT significantly enhances the capacity retention of LNMO||artificial graphite (AG) pouch cells from 24.71% to 82.73%. Multiscale characterizations reveal that BDTT undergoes preferential reduction at the anode, contributing to the formation of a sulfur-rich and compact solid electrolyte interphase (SEI), which effectively lowers interfacial resistance and suppresses cross-talk effects.