Machine learning predicting the effects microstructures of biomass hard carbon have on the electrochemical performance of SIBs anodes.

This study involved the development of machine learning models to investigate how the microstructures of biomass-based hard carbon (HC) influence sodium storage mechanisms and performance. The goal was to pinpoint the key microstructural features of biomass-based HC that impact the initial coulombic efficiency (ICE) and reversible capacity (RC) of sodium-ion batteries (SIBs). To achieve this, a database was established to correlate structural parameters of HC with essential sodium storage performance metrics (referred to as the Hard Carbon-SIBs, HCSs database). The XGBoost model exhibited high accuracy and excellent generalization in predicting both RC and ICE, achieving coefficients of determination (R) of 0.88 and 0.77, respectively. SHAP analysis indicated that variations in specific surface area (SSA) significantly affected both electrochemical properties, while PDP analysis identified the key input features influencing RC and ICE. The findings suggest that this methodology holds significant promise for advancing the development of electrochemical energy storage materials.