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

arXiv: https://arxiv.org/abs/2409.01989 · HTML: https://mlsanity.com/paper/2409.01989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Large-Scale Chemical Language Representations Capture Molecular Structure and Properties](https://mlsanity.com/paper/2106.09553/) (2021)
- [A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME](https://mlsanity.com/paper/2305.02012/) (2023)
- [Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning](https://mlsanity.com/paper/2111.14786/) (2021)
- [Accelerating Material Design with the Generative Toolkit for Scientific Discovery](https://mlsanity.com/paper/2207.03928/) (2022)
- [Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance](https://mlsanity.com/paper/2307.03811/) (2023)

## Abstract (arXiv)

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.