An Interpretable Machine Learning Framework for Predicting the Electrochemical Properties of Carbon-Based Electrode Materials in Lithium-Ion Batteries

doi:10.22034/jmrph.2026.137621.1000

An Interpretable Machine Learning Framework for Predicting the Electrochemical Properties of Carbon-Based Electrode Materials in Lithium-Ion Batteries

Document Type : Original Article

10.22034/jmrph.2026.137621.1000

Abstract

In this study, machine learning methods based on quantum chemical descriptors derived from Density Functional Theory (DFT) calculations were employed to predict the redox potentials of redox-active organic molecules. Electronic descriptors extracted from DFT calculations served as inputs for various machine learning models, whose performances were evaluated using R2, MAE, and RMSE metrics. The results demonstrated that the SVR model achieved the highest predictive accuracy, with a coefficient of determination (R2) of 0.974 and the lowest error rate, while the Stacking model exhibited closely comparable and stable performance. Correlation analysis, feature importance, and SHAP values revealed that electron transfer-related descriptors, particularly electron affinity, play the most significant role in predicting redox potentials. The consistency between ML results and quantum chemical concepts indicates that the proposed framework achieves high predictive accuracy alongside robust interpretability. This study highlights that the integration of DFT calculations and machine learning can provide an efficient approach for the rapid screening and targeted design of redox-active organic molecules with optimized electrochemical properties.

Keywords

Density Functional Theory

Machine Learning

Redox Potential

Organic Molecules

Subjects

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