Toward accelerated electrocatalyst design: synergistic integration of DFT, machine learning, and microkinetic modeling.
TL;DR
Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.
Abstract
Electrocatalysis plays a pivotal role in sustainable energy conversion technologies; however, the rational design of high-performance electrocatalysts remains challenging because of complex reaction mechanisms, multiscale kinetics, and the vast chemical space of candidate materials. This review highlights the synergistic integration of density functional theory (DFT), machine learning (ML), and microkinetic modeling (MKM) as a unified framework for accelerating electrocatalyst discovery. We first discuss the role of DFT in elucidating electronic structures, adsorption energetics, reaction mechanisms, and descriptor development. We then examine recent advances in ML for high-throughput catalyst screening, descriptor engineering, feature selection, property prediction, uncertainty quantification, and autonomous discovery workflows. The role of MKM in bridging atomistic energetics with experimentally relevant quantities, including reaction rates, turnover frequencies, selectivity, and surface coverages, is subsequently discussed. Representative applications of integrated DFT-ML-MKM frameworks for the rational design of single-atom, dual-atom, and multifunctional electrocatalysts for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR) are highlighted. Finally, current challenges-including data quality, descriptor selection, model transferability, interpretability, realistic electrochemical modeling, and multiscale integration-are critically assessed. Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.