Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning

TL;DR

This paper introduces DiffMix, a differentiable geometric deep learning model for battery electrolyte mixtures, which improves prediction accuracy and guides robotic experiments to optimize electrolytes for fast-charging batteries.

Contribution

The work develops a novel GDL-based model that incorporates physical laws for chemical mixtures, enabling accurate predictions and efficient optimization of battery electrolytes.

Findings

DiffMix outperforms purely data-driven models in thermodynamics and ion transport predictions.

Robotic experimentation with Clio increased electrolyte ionic conductivity by over 18.8%.

The approach accelerates chemical mixture optimization using gradients from DiffMix.

Abstract

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.