2026-10-12T09:47:16Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33994058 2026-09-25T08:33:51Z category_4 category_7 category_873 category_734 category_64 portal_63 item_type_3 month_year_09_2026

Screening “Cathode-Stable and Anode-Reactive” Sulfur-Based Additives for LiNi_0.5Mn_1.5O₄||Graphite Pouch Cells via a Machine Learning Approach Jinlong Sun (783488) Shinuo Kang (15454167) Xiaobing Lou (2734198) Ming Shen (156685) Bingwen Hu (1305561) Biochemistry Medicine Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Cancer provides new insights multiscale characterizations reveal molecular descriptor analysis gradient boosting regression functional additive discovery electrochemical evaluations demonstrate 45 ° c screening “ cathode bdtt significantly enhances 5 </ sub machine learning approach reactive ” sulfur voltage spinel lini 4 </ sub machine learning talk ” voltage lithium voltage conditions work validates talk effects suppresses cross sei ), promising candidate pouch cells ion batteries energy levels elevated temperature electrolyte design electrolyte decomposition capacity retention based additives 85 v 73 %. For high-voltage spinel LiNi_0.5Mn_1.5O₄ (LNMO) systems, under elevated temperature and high-voltage conditions, electrolyte decomposition and the associated interfacial “cross-talk” between the cathode and anode severely compromise cycling stability. 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. This work validates the effectiveness of machine learning in functional additive discovery and provides new insights into electrolyte design for high-voltage lithium-ion batteries. 2026-09-25T00:00:00Z Dataset Dataset 10.1021/acs.jpclett.6c02511.s001 https://figshare.com/articles/dataset/Screening_Cathode-Stable_and_Anode-Reactive_Sulfur-Based_Additives_for_LiNi_sub_0_5_sub_Mn_sub_1_5_sub_O_sub_4_sub_Graphite_Pouch_Cells_via_a_Machine_Learning_Approach/33994058 CC BY-NC 4.0