Integration of Density Functional Theory and Machine Learning for Electrolyte Optimization in High-Voltage Dual-Ion Battery Design.

Abstract

Designing dual-ion batteries (DIBs) by using various electrolytes through experiments or computationally is highly time-consuming and needs high-cost sophisticated resources. Here, we have utilized the ultrafast screening capability of machine learning (ML) to search for suitable salt-electrolytes toward the design of DIBs, choosing voltage as the desirable descriptor. Considering 50 different salts and their suitable staging mechanisms, the XGBoost Regressor ML model has been found to perform with remarkable accuracy. This is further validated by density functional theory, cross-validation, and experimental findings. An interpretable ML technique has been employed for local and global feature analysis to interpret the ML predicted results, underscoring the importance of choosing input features. This ML assisted DIB design approach has the potential to explore unknown salt-electrolytes that have yet to be tested in DIBs. Finally, we introduce the predicted voltages for all of the salt-electrolyte combinations as well as their probable staging mechanism. We signify the absence of a general trend in the predicted voltages, as various combinations of cations and anions are found to deliver unique voltages. Our study can guide researchers toward tuning constituent salts as well as staging mechanisms for the design of efficient DIBs.