Rational Design of Carbon‐Based Electrocatalysts for H2O2 Production by Machine Learning and Structural Engineering

This article reviews the rational design of carbon-based electrocatalysts for H₂O₂ production via machine learning and structural engineering. Carbon-based catalysts, low-cost and tunable, are promising alternatives to noble metal catalysts. Machine learning and DFT accelerate catalyst screening and structure prediction. However, challenges in industrial scaling persist, necessitating multidisciplinary efforts to develop robust catalysts for diverse operating conditions.

Abstract

Electrochemical synthesis of hydrogen peroxide (H₂O₂) via two-electron oxygen reduction reaction (2e⁻ ORR) represents an economically viable alternative to conventional anthraquinone processes. While noble metal catalysts have dominated this field, carbon-based materials are emerging as promising alternatives due to their low cost, abundant reserves, and tunable properties. This mini-review summarizes recent advances in computational methods, particularly the integration of density functional theory (DFT) with machine learning (ML), to accelerate the rational design of electrocatalysts by enabling rapid screening and structure-training predictions. Meanwhile, the optimization strategies of carbon-based electrocatalysts are systematically investigated, focusing on four key aspects: atomic-level heterochromatic doping, defect engineering, microenvironment control, and morphological design. Despite significant progress in achieving high selectivity and activity, challenges remain in scaling these materials for industrial applications. Moving carbon-based H₂O₂ electrocatalysts will require multidisciplinary efforts combining advanced in situ characterization techniques, computational modeling, and process engineering to develop robust catalysts suitable for diverse operating conditions.