journal article·2026-02-13

Differentiable Electrochemistry: A Paradigm Characterizing Physical Laws in Electrochemical Systems

Haotian Chen, Chenyang Huang, Alexander Rodríguez, Aashutosh Mistry, Venkatasubramanian Viswanathan

ACS Energy Letters· Volume 11· Issue 2
Publisher page

Abstract

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.
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Research context

OpenAlex topics: CO2 Reduction Techniques and Catalysts · Electrocatalysts for Energy Conversion · Advanced Battery Materials and Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Michigan Institute for Data and AI in Society

University of Michigan

Department of Aerospace Engineering

Department of Mechanical Engineering

Division of Computer Science and Engineering

Colorado School of Mines

Payne Institute for Public Policy

Funding

Schmidt AI in Science Fellowship Program, Schmidt Sciences, LLC

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