Utilizing Machine Learning to Predict the Charge Storage Capability of Lithium-Ion Battery Materials

With the increasing demand for high-performance batteries in applications such as electric vehicles and portable electronics, accurately predicting the charge storage capacity of battery materials is crucial for developing more efficient and reliable energy storage systems. Machine Learning (ML) and data-driven approaches, plays a vital role in enhancing our understanding of Li-ion battery performance, guiding materials design, optimizing system efficiency, and accelerating innovation in energy storage technologies. In this study, an ML-based approach was applied to a dataset of 2345 rechargeable Li-ion battery materials, obtained from the Materials Project online portal, to predict gravimetric charge storage capacity & horbar; a key parameter for energy storage capability. To model this relationship, three key independent features were selected: average operating voltage, gravimetric energy density, and charging stability. Given the nonlinear dependencies between these features and the target variable, an ensemble learning algorithm, Gradient Boosting Regression (GBR), was employed. The model exhibited high predictive accuracy, achieving an R2 value of 0.99 on the test dataset with a Mean Squared Error (MSE) of 20.08 for target feature values. These results confirm the model's effectiveness in capturing complex relationships within the battery materials dataset, demonstrating its reliability in predicting charge storage capacity with minimal error. The feature selection strategy emphasizes practical electrochemical properties, enhancing the model's interpretability and relevance for battery material screening. Its low error metrics indicate strong generalizability, positioning it as a valuable tool for accelerating battery material discovery and optimizing performance. This study distinguishes itself by focusing on gravimetric charge storage capacity prediction using domain-relevant features and an ensemble learning approach, leveraging a large open-source dataset to achieve high predictive accuracy. This is crucial for energy storage capabilities, but it has been less frequently modeled directly in ML-driven battery studies.