Material insights for supercapacitor designs: A shapley additive explanatory study of critical temperature dependence on atomic properties

The design of high-performance superconducting materials for energy storage applications relies on a deep understanding of the relationship between material properties and superconducting critical temperature ( T c ) dependence. This study employs a Shapley Additive Explanatory ( SHAP ) approach to investigate the impact of atomic properties on the T c of supercapacitors materials relevant to supercapacitor electrode applications. A vast dataset of crystalline supercapacitor materials is utilized to train a range of machine learning models, including Random Forest, Bagging Regressor, Extra Trees, and K-Nearest Neighbor. The top-performing models demonstrate high predictive accuracy, with R 2 values ranging from 0.50 to 0.56 and RMSE values between 17.74 and 18.90. The SHAP analysis reveals that Average Electronegativity has the most significant impact on the T c measurements, indicating its crucial role in determining the performance of supercapacitors. Furthermore, the descriptor impact study shows that the Target Property Function, f(x), also known as the T c Function, is equal to 8.199, while the Expected Value of the Target Property, E[f(X)], ranges from 8 to 22. This suggests that the T c of supercapacitors is highly dependent on the atomic properties of the materials, and that Average Electronegativity is a key factor in determining their performance.