Disentangling multifactorial impacts on cathode thermochemical properties with explainable machine learning

Abstract

Thermal safety remains a critical concern in the commercialization of lithium-ion batteries (LIBs), with extensive research dedicated to understanding the thermal behaviors of cathode materials. While a wealth of thermochemical test data is available in the literature, the variability in sample conditions and experimental testing parameters complicates the identification of fundamental relationships between the intrinsic properties and thermochemical reaction characteristics of materials. This study utilizes explainable machine learning (ML) methodologies to tackle this challenge by analyzing a comprehensive database derived from published differential scanning calorimeter (DSC) testing results. By employing meticulously curated, augmented, and filtered features that characterize material properties, sample conditions, and testing parameters, we leveraged ML models to predict and validate thermochemical reaction characteristics across the chemical compositional space of layered oxide cathode materials. Through the explainability, we elucidated multidimensional relationships between input features and thermochemical reaction characteristics, revealing that material properties predominantly dictate the initiation of the reaction, while external conditions exert a greater influence on the kinetics of heat release. This approach demonstrates the effectiveness of ML in decoding complex causal factors of cathode thermochemical reaction behaviors, thereby offering valuable insights for targeted thermal optimization in battery safety design.