Leveraging Machine Learning for Screening Metal-Organic Frameworks with Selective CO2 Recognition for Early Thermal Runaway in Lithium-Ion Batteries

作者：Xian Wei, Xin Li, Xiong Wang, Xiaoyan Liu, Chen Zhu · 发表于：Nanomaterials · 年份：2026 · DOI：10.3390/nano16040245 · 研究领域：Medicine

The escalation of thermal runaway in lithium-ion batteries presents severe safety hazards that necessitate advanced monitoring protocols to ensure early warning of potential failures. Carbon dioxide (CO2) is released during preliminary decomposition well before catastrophic failure occurs, thereby providing a strategic advantage for early-stage warning. Consequently, identifying materials with high-selective CO2 recognition is an essential prerequisite for developing reliable sensing platforms. This study integrates Grand Canonical Monte Carlo simulations with Random Forest (RF) models to systematically screen 1470 MOFs from the CoRE-MOF 2019 database. The screening process evaluates selective CO2 recognition under multicomponent competitive adsorption conditions involving CO2, C2H4, and O2. The performance evaluation is based on working capacity, selectivity, and the trade-off between working capacity and selectivity (TSN). The RF model achieves high predictive accuracy, with tested R2 exceeding 0.92 on the test samples. Shapley Additive Explanations (SHAP) interpretability analysis identifies Q0st(CO2), Q0st(C2H4), WEPA, KH(C2H4), and ETR as key performance drivers. The results indicate that CO2 selectivity is constrained by the binding strength of competing C2H4. Optimal materials tend to have hard Lewis acid centers and polar inorganic clusters to minimize non-specific π-interactions with interfering species. Top-performing MOFs require balanced structural features, conce...