Unlocking the Potential of Dual-Ion Batteries: Identifying Polycyclic Aromatic Hydrocarbon Cathodes and Intercalating Salt Combinations through Machine Learning.

Abstract

Dual-ion batteries (DIBs) represent a promising energy storage technology, offering a cost-effective safe solution with impressive electrochemical performance. The large combinatorial configuration space of the electrode-electrolyte leads to design challenges. We present a machine learning (ML) approach for accurately predicting the voltage and volume changes of polycyclic aromatic hydrocarbon (PAH) cathodes upon intercalation with a variety of DIB salts following different mechanisms. Gradient Boosting and XGBoost Regression models trained on the data set demonstrate exceptional performance in voltage and volume change prediction, respectively. The models are further cross-validated and utilized to predict the properties for ∼700 combinations of PAH and DIB salt intercalations, a subset of which is further validated by density functional theory. Using average voltage and volume change for all combinations of PAHs and salts, preferable combinations for high/low voltage requirements along with long-term stability are obtained. Overall, the study shows the applicability of PAHs in DIBs exhibiting good electrochemical performance with low volume change compared to graphite indicative of its potential to overcome the cycling stability issues of DIBs. This research establishes a reliable and broadly applicable ML-based workflow for efficient screening and accelerated design of advanced PAH cathodes and salts, thus driving progress in the field of DIBs.