Machine Learning-Driven Rational Design of Organic Electro-Oxidation Catalysts: A Mechanism-Embedded Approach to Selectivity Engineering.

Abstract

Organic electro-oxidation (OEO) uses electrons to drive selective organic transformations under ambient conditions, offering a sustainable alternative to high-temperature, high-pressure oxidation and contributing to the decarbonization of chemical manufacturing. However, OEO systems typically break linear scaling relationships due to coupled multi-pathway competitive kinetics, dynamic interfacial restructuring, and cross-scale amplification, rendering reaction networks highly nonlinear and high-dimensional. Traditional approaches, single thermodynamic descriptors, trial-and-error optimization, and ex situ characterization are thus insufficient. This review examines three representative OEO systems, urea oxidation, 5-hydroxymethylfurfural oxidation, and glycerol oxidation, which exhibit distinct mechanistic control requirements, and establishes connections among competing reaction barriers, proton-coupled electron transfer, and ML descriptor construction. We formulate a mechanism-embedded machine learning (ME-ML) perspective by integrating existing advances in mechanism-informed descriptors, physics-constrained modelling, and multi-objective optimization. We further evaluate ML applications under industrially relevant conditions, including catalyst lifetime prediction, techno-economic analysis, and automated closed-loop discovery, and discuss the extension of ME-ML toward dynamic electrochemical interface representation. By reorganizing mechanistic knowledge as an integral component of ML-driven catalyst discovery, ME-ML provides a systematic framework to connect molecular-level understanding with scalable OEO process development.