Improving Electrolyte Performance for Target Cathode Loading Using Interpretable Data-Driven Approach

TL;DR

This study employs a data-driven, interpretable deep learning approach to optimize electrolyte formulations, significantly improving battery capacity at high cathode loadings for a novel interhalogen battery.

Contribution

It introduces a graph-based deep learning model for electrolyte optimization, combining experimental data with interpretability to enhance battery performance.

Findings

Achieved a 20% increase in specific capacity through data-driven optimization.

Identified design principles for electrolytes at different cathode loadings.

Demonstrated the effectiveness of combining experimental data with deep learning for battery design.

Abstract

Higher loading of active electrode materials is desired in batteries, especially those based on conversion reactions, for enhanced energy density and cost efficiency. However, increasing active material loading in electrodes can cause significant performance depreciation due to internal resistance, shuttling, and parasitic side reactions, which can be alleviated to a certain extent by a compatible design of electrolytes. In this work, a data-driven approach is leveraged to find a high-performing electrolyte formulation for a novel interhalogen battery custom to the target cathode loading. An electrolyte design consisting of 4 solvents and 4 salts is experimentally devised for a novel interhalogen battery based on a multi-electron redox reaction. The experimental dataset with variable electrolyte compositions and active cathode loading, is used to train a graph-based deep learning model mapping changing variables in the battery's material design to its specific capacity. The trained model is used to further optimize the electrolyte formulation compositions for enhancing the battery capacity at a target cathode loading by a two-fold approach: large-scale screening and interpreting electrolyte design principles for different cathode loadings. The data-driven approach is demonstrated to bring about an additional 20% increment in the specific capacity of the battery over capacities obtained from the experimental optimization.