A data-driven machine learning approach for predictive modeling of transition metal dichalcogenide/carbon composite supercapacitor electrodes

Transition metal dichalcogenide (MS₂) materials possess unique pseudocapacitive characteristics but typically suffer from poor electrical conductivity and volume expansion during cycling, limiting their practical applications. Combining MS₂ with conductive carbon materials has proved to be a common and effective strategy, yielding MS₂/carbon composites with significantly improved supercapacitor performance. However, their development often relies on empirical trial-and-error methods, limiting systematic progress. Machine learning (ML) is revolutionizing materials science by enabling the rapid screening and prediction of material properties. In this study, we present a comprehensive ML framework to predict the electrochemical performance of MS₂/carbon composite supercapacitor electrodes. Four ML models were evaluated, with the transformer-based TabPFN model achieving the highest predictive accuracy (R² = 0.988 and RMSE = 32.15 F g⁻¹). SHapley Additive exPlanations (SHAP) identified covalent radius, specific surface area, and current density as critical factors governing the specific capacitance (C_s). Density functional theory (DFT) calculations were performed to evaluate the adsorption energies of potassium ions on various MS₂ slabs, and the agreement with the ML results confirms the reliability of the ML predictions. This work establishes a data-driven ML approach to guide the design of advanced pseudocapacitive materials, significantly accelerating their development.