Despite the long history of electrochemistry, there is a lack of quantitative algorithms that rigorously correlate experiments with theory. Here we introduce Differentiable Electrochemistry, an emerging 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. Specifically, Differentiable Electrochemistry advances beyond the Tafel and Nicholson methods by removing several limitations including Tafel region selection and identifies the electron transfer mechanism in Li metal electrodeposition/stripping by parametrizing the full Marcus–Hush–Chidsey formalism. In addition, Differentiable Electrochemistry interprets Operando X-ray measurements in a 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.
Chen et al. (Tue,) studied this question.