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.
Metal organic frameworks (MOFs) have emerged as promising electrode materials for supercapacitor (SC) due to their high surface areas, tunable porosity, and redox active sites. However, the vast chemical space of MOFs leads to millions of possible structures, makes experimental trial and error discovery inefficient. This review provides a focused perspective on how density functional theory (DFT) and machine learning (ML) are enabling the accelerated discovery and rational design of MOF-based SC electrodes. Key insights from DFT are discussed in relation to three critical performance descriptors as electrical conductivity, electrochemical and structural stability, and redox activity. In parallel, recent advances in ML-driven screening are reviewed, covering the development and use of large-scale MOF databases, descriptor engineering strategies, and predictive model architectures. Case studies demonstrating the successful integration of DFT and ML for identifying high-performance MOFs are highlighted in the review. Finally, the current limitations are analysed, including the discrepancy between idealized computational models and real polycrystalline electrodes, intrinsic trade-offs between conductivity and stability, and the need for interpretable and physics-informed ML models. Overall, this review outlines a computational roadmap for the rapid discovery and optimization of next-generation MOF-based SC electrodes. This is the comprehensive review on ML-driven prediction of electrochemical performance in MOF based electrode for SC applications. It integrates DFT-calculated electronic descriptors with ML models to discover hidden structure-property relationships. It identifies critical data gaps, model transferability issues, lack of dynamic ion-transport modelling in current studies. It proposed a multi-fidelity active learning framework combining DFT, ML and experiments for accelerated MOF discovery. It outlines standardized database protocols and explainable AI strategies to guide future high-performance MOF design.
Achal Siddharth Fulmali, H. Panda· Journal of Materials Science...· 0 citations
The production of green hydrogen through water splitting requires highly efficient electrocatalysts, but the current trial-and-error-based synthesis or discovery is time-consuming, costly and resource-intensive. Machine learning (ML) provides a powerful, data-driven alternative that can model complex structure-activity relationships across large chemical spaces at orders-of-magnitude speed. This review systematically overviews the life cycle of the electrocatalyst research and development application of ML. First, the thermodynamic and kinetic principles of the hydrogen and oxygen evolution reactions are summarised, along with some well-adopted and accepted activity descriptors. Then we explore data sources, featurization approaches, and algorithms, and discuss the model space, from a simple interpretable model to a graph neural network to a generative model, in the context of the ML toolkit. Strategic applications are discussed for high-throughput virtual screening of alloys and single-atom catalysts, as well as multifunctional activity prediction for overall water splitting, and stability optimisation under operating conditions. The topic of emerging frontiers is highlighted, including high-entropy alloys, amorphous materials, and linking atomic-scale understanding to device-level performance through integration with density functional theory. Finally, the problems of data scarcity, model interpretability and the discrepancy between computational predictions and industrial implementation are discussed, along with future directions for closed-loop discovery and self-driving laboratories. Incorporating ML into electrocatalyst design and combining it with autonomous experimentation will revolutionise this process, from simulation to energy solution, dramatically speeding it up.
Vamsi Krishna Kudapa, Shoaib Mohd, Vijayakumar Sivasundar et al.· Frontiers in Chemistry· 0 citations
Dual-atom catalysts (DACs) provide a powerful platform for oxygen electrocatalysis, yet rational design remains limited by the lack of transferable mechanistic principles. Machine learning (ML) has the potential to address this gap, yet its role in mechanistic discovery remains largely underexplored despite its wide use in catalyst screening. Here, using extended phthalocyanines (M1M2-ePc), we establish an integrated DFT-ML-experiment framework that maps catalytic performance onto an interpretable electronic landscape. Screening 81 DFT-computed and 360 ML-predicted metal pairs identifies FeM-ePc as a promising bifunctional catalyst family. Notably, SHapley Additive exPlanations (SHAP) analysis highlights the importance of electronic background and the key role of the secondary metal in regulating catalytic activity. First-principles calculations further uncover a cooperative dual-descriptor mechanism, in which d-band center and charge transfer jointly govern bifunctional activity. Combining LASSO with SISSO yields compact analytical formulas that quantitatively reproduce ηORR and ηOER, providing interpretable descriptors for DACs. Guided by these findings, FeCo-ePc-L with atomically dispersed Fe-Co sites was synthesized to experimentally examine the ML-guided prediction. This work highlights the utility of interpretable ML for mechanistic discovery in DACs by revealing role-asymmetric electronic cooperation between paired metal centers.
Shao-Bo Jia, Lu Yang, Chou Wu et al.· Advances in Materials· 0 citations
Two‐Stage Material Screening (TSMS) is developed, an AI‐driven framework that integrates density functional theory (DFT) computations, an active‐learning‐guided experimental feedback loop, and mechanistic interpretation to enable rapid discovery and systematic evaluation of promising electrocatalysts.
Xueyu Hu, Yucun Zhou, Haoyu Li et al.· Advances in Materials· 0 citations
Machine learning has significantly reduced the computational power necessary to estimate the free energy of adsorption of key reaction intermediates on a diverse range of catalytic surfaces. Nevertheless, translating this computational capability into the discovery and experimental validation of catalysts necessitates targeting specific questions where this faster computation can yield the most significant impact. Using the electrochemical oxygen reduction reaction (ORR) as a model reaction due to its known linear scaling relationships between key catalytic intermediates, we demonstrate that the Open Catalyst Project’s machine-learning-based calculations of adsorption energies can inform experimental catalytic research. The primary challenge we addressed was the structural effect of the ORR, wherein the higher Miller index facets of Pt exhibit diminished ORR activity in comparison to Pt(111). The Open Catalyst Project’s rapid relaxation energy calculations enabled us to screen a large number of bimetallic materials for each key intermediate of the ORR over a wide range of crystal facets. The Open Catalyst Project was able to identify that PdAu3 can overcome the negative structural effects observed on the higher Miller index facets of Pt for the ORR. Electrochemical experimentation via rotating disk electrode linear sweep voltammetry and Tafel slope analysis revealed that polycrystalline PdAu3 nanoparticles exhibit an improved onset potential for the ORR compared to commercial polycrystalline Pt/C. Thus, this study demonstrates an example of how machine-learning-driven materials discovery can accelerate catalyst design while simultaneously offering an experimental solution to the persistent challenge of high ORR overpotential.
Darik A. Rosser, Anto Felix Sotvik GS, Kevin C. Leonard· ACS Applied Energy Materials· 0 citations
Electrocatalytic nitric oxide reduction reaction (NORR) for ammonia synthesis has emerged as a research focus in artificial nitrogen fixation. Unlike previous reviews that primarily focus on experimental catalyst development, this work offers a comprehensive and systematic summary of recent theoretical progress in NORR, with special emphasis on low-dimensional materials. We connect four important areas: atomic-level design principles for active sites, emerging mechanistic ideas that go beyond conventional scaling relations, realistic simulations of the electrochemical microenvironment, and data-driven machine learning approaches for catalyst discovery. We begin by discussing the reaction mechanism, analyzing the orbital interactions that control NO activation and the thermodynamic and kinetic features of different reaction pathways. For active-site construction, we examine electronic synergy in single-atom and dual-atom catalysts, coordination microenvironment tuning, electronic structure modulation through doping and strain, and heterojunction interfaces that allow multi-degree-of-freedom regulation. To explore new mechanistic concepts, we introduce p-block element synergy, reverse activation, magnetic and spin control, and surface electronic singularities as strategies to overcome traditional scaling relations. Regarding the reaction microenvironment, we analyze how coverage, solvation, local pH, and applied potential jointly affect selectivity and activity. Finally, we summarize the role of machine learning in building descriptors and accelerating catalyst screening. This review aims to provide theoretical guidance for the rational design of efficient NORR electrocatalysts with high activity, selectivity, and long-term stability.
Yu Liang, Daoming Zhang, Weiyi Wang et al.· Crystals· 0 citations