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.