Accelerating single-atom ORR catalyst discovery through theory and machine learning - a critical review

Aug 4, 2026

The oxygen reduction reaction (ORR) underpins efficiency in fuel cells and metal-air batteries, making high-efficiency, low-cost electrocatalysts essential. Single-atom catalysts (SACs) offer optimized atom economy, precise coordination environments, and controllable electronic structures for ORR. Density functional theory (DFT) has guided mechanistic understanding and structure–activity relationships, yet the vast SAC composition and structure search space challenges purely theoretical workflows. This review compiles theory-guided and machine-learning (ML) assisted strategies that leverage DFT data to accelerate rational SAC discovery and evaluate ORR energetics and activity. It highlights ORR descriptors, selectivity and stability at isolated metallic sites, and focuses on M–N–C catalysts, coordination asymmetry, axial ligation, dual-descriptor frameworks, and the use of graph neural networks, concluding with key limitations and future outlook toward closed-loop autonomous catalyst discovery and experimental validation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages29

Reading Time1 h 13 min

AccessFree to download

Introduction

Broader context and motivation with ORR Electrocatalysis

ORR fundamentals and theory-based descriptors

Machine-learning and high-throughput strategies for SAC discovery

Supervised learning, descriptor-based approaches, high-throughput, and generative modeling

Focus areas: M–N–C catalysts and advanced structural factors

Coordination asymmetry and axial ligation

Dual-descriptor frameworks and graph neural networks

Challenges and future perspectives

Data scarcity, interpretability, transferability, and dynamic effects

Coordination Chemistry Reviews 560 (2026) 217870 Contents lists available at ScienceDirect Coordination Chemistry Reviews journal [homepage: www.elsevier.com/locate/ccr](homepage: www.elsevier.com/locate/ccr) | Accelerating single-atom ORR catalyst discovery through theory and machine learning: a critical review
Anuj Kumar a,d,1,*, Parth Bishnoia,1, Akshay Parmara,1, Mohammad Khalidc,*, Mohd Ubaidullah b, Abdullah M. Al-Enizi b,*
a Nano-Technology Research Laboratory, Department of Chemistry, GLA University, Mathura, Uttar Pradesh 281406, India b Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
c James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
d Institute of General and Inorganic Chemistry of Uzbekistan Academy of Sciences, 100170, Mirzo Ulug’bek str., 77a Tashkent, Uzbekistan | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Single-atom catalysts Atomic economy DFT
Machine learning Energy conversion ORR | | The oxygen reduction reaction (ORR) is an important efficiency-determining process in fuel cells and metal-air batteries, requiring highly efficient and low-cost electrocatalysts. Single-atom catalysts (SACs) have recently emerged as a revolutionary type of catalyst for ORR reaction due to their optimized atom use, precise coordination environment, and controlled electronic structure. Theoretical simulations, especially density functional theory (DFT), have been highly influential in understanding mechanisms of ORR reaction and defining structureactivity relationships of SACs during the last few years. Nevertheless, a very substantial number of possible SAC compositions and structures represents an intrinsic difficulty of solely theoretical catalyst research. In this context, synergy of machine learning (ML) with DFT results has emerged a new paradigm with high potential for speeding up the rational design of SACs for ORR. This review presents a compilation of the latest results on the theory-guided and ML-assisted investigations on ORR active SACs. First, basic concepts of ORR, theory-based descriptors for ORR activity, selectivity, and stability at isolated metallic sites are reviewed. Next, the latest approaches based on ML, including supervised machine learning, descriptor-based approaches, high-throughput techniques, and generative modeling, which use DFT-based data to quickly estimate the ORR energetics of promising SAC structures. Particular attention is given to M–N–C catalysts, coordination asymmetry, axial ligation, frameworks described by dual descriptors, and the role of graph neural networks for the local chemical environments. Finally, we also payed special attention to the challenges, such as the lack of sufficient data, interpretability, and the aforementioned transferability and dynamic effects. Concluding, we provided future perspectives on closed-loop autonomous catalyst discovery, the theory, machine learning, and experimental validation for the development of the next generation ORR electrocatalysts. | 1. Introduction 1.1. Broader context and motivation with ORR Electrocatalysis The pace at which the transition towards sustainable and carbon neutral energy has shifted around the globe has led to the positioning of electrochemical energy conversion systems, including fuel cells and metal-air batteries, at the cutting edge of current energy innovations [1–4]. The ORR process has played a crucial role in defining the efficiency of these systems because it is a multi-step and kinetically slower electrochemical reaction [5–7]. The absence of catalysts possessing capabilities of high activity and selectivity towards the four-electron ORR process has remained a severe challenge for decades [8–10]. In this regard, various platinum group metal catalysts have traditionally been considered the excellence in ORR by virtue of their superior adsorption energetics and catal

FAQs

Why is the oxygen reduction reaction (ORR) critical for energy devices?

ORR largely determines the efficiency of fuel cells and metal-air batteries. It is multi-step and kinetically slower, so catalyst performance directly impacts practical energy conversion.

What advantages do single-atom catalysts (SACs) provide for ORR?

SACs maximize atom efficiency and provide atomically precise active-site definitions. Their optimized coordination environment and tunable electronic structure support improved ORR activity and selectivity.

How does machine learning enhance DFT-based catalyst discovery for ORR-active SACs?

ML combined with DFT data speeds up the screening of promising SAC structures by quickly estimating ORR energetics. Approaches include supervised learning, descriptor-based methods, high-throughput workflows, and generative modeling.

Aug 4, 2026

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