Differentiable Electrochemistry: A paradigm for uncovering hidden physical phenomena in electrochemical systems

TL;DR

Differentiable Electrochemistry introduces a novel framework that integrates thermodynamics, kinetics, and mass transport with automatic differentiation, enabling gradient-based optimization for mechanistic discovery in electrochemical systems.

Contribution

This work develops the first end-to-end differentiable electrochemical simulation framework, bridging data-driven and physics-based models for improved analysis and discovery.

Findings

Achieves 10-100x improvement over gradient-free methods.

Removes limitations of traditional Tafel and Nicholson methods.

Identifies electron transfer mechanisms in Li metal electrodeposition.

Abstract

Despite the long history of electrochemistry, there is a lack of quantitative algorithms that rigorously correlate experiment with theory. Electrochemical modeling has had advanced across empirical, analytical, numerical, and data-driven paradigms. Data-driven machine learning and physics based electrochemical modeling, however, have not been explicitly linked. Here we introduce Differentiable Electrochemistry, a mew paradigm in electrochemical modeling that integrates thermodynamics, kinetics and mass transport with differentiable programming enabled by automatic differentiation. By making the entire electrochemical simulation end-to-end differentiable, this framework enables gradient-based optimization for mechanistic discovery from experimental and simulation data, achieving approximately one to two orders of improvement over gradient-free methods. We develop a rich repository of differentiable simulators across diverse mechanisms, and apply Differentiable Electrochemistry to bottleneck problems in kinetic analysis. Specifically, Differentiable Electrochemistry advances beyond Tafel and Nicholson method by removing several limitations including Tafel region selection, and identifies the electron transfer mechanism in Li metal electrodeposition/stripping by parameterizing the full Marcus-Hush-Chidsey formalism. In addition, Differentiable Electrochemistry interprets Operando X-ray measurements in concentrated electrolyte by coupling concentration and velocity theories. This framework resolves ambiguity when multiple electrochemical theories intertwine, and establishes a physics-consistent and data-efficient foundation for predictive electrochemical modeling.