Accelerating sodium-ion electrode material development through AI-driven optimization and predictive modeling

Sodium-ion batteries (SIBs) are gaining traction as a cost-effective and sustainable alternative to lithium-ion batteries for large-scale energy storage, due to sodium’s abundance, low cost, and safety advantages. However, the discovery of high-performance electrode materials for SIBs remains a significant challenge because of the complex interactions between compositional and structural features that govern key properties such as specific capacity, average voltage, and volume change. In this study, we present an artificial intelligence (AI)-driven framework that integrates machine learning and multi-objective optimization to accelerate the design of sodium-ion battery electrodes. Four predictive models, namely Decision Tree, Random Forest, Support Vector Machine (SVM), and Deep Neural Network (DNN), were trained on a feature-rich dataset derived from high-throughput computational databases. The DNN model achieved the highest predictive accuracy, with R 2 values up to 0.97 and mean absolute errors (MAE) below 0.11 for the target properties. To support material selection, the DNN was coupled with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify Pareto-optimal materials that maximize specific capacity while minimizing volume expansion. The resulting candidates exhibit balanced electrochemical performance and potential for practical SIB applications. This study demonstrates the power of combining deep learning and optimization to guide the discovery of next-generation energy storage materials with high efficiency and reduced experimental overhead. • Artificial intelligence accelerates discovery of battery electrode materials, predicting key metrics. • A Deep Neural Network (DNN) model achieves high accuracy in predicting capacity, volume change, and voltage. • Integrating DNN with NSGA2 optimization identifies Pareto-optimal solutions for battery material properties.