Jul 2026· Journal of Multiscale Materials Informatics· Vol 3, pp. 1-6· 0 citations· 29 references
TL;DR
This review systematically summarizes recent advances in ML-assisted OER research, focusing on key aspects including dataset construction, descriptor engineering, model development, and performance evaluation, and focuses on the role of physicochemical descriptors in governing model performance and interpretability.
Abstract
The Oxygen Evolution Reaction (OER) is a fundamental process in electrochemical water splitting, playing a crucial role in sustainable hydrogen production. However, its intrinsically sluggish kinetics, involving complex four-electron transfer steps, remain a major bottleneck for efficient energy conversion. In recent years, Machine Learning (ML) has emerged as a powerful approach to accelerate catalyst discovery by enabling data-driven prediction of OER activity and reducing reliance on costly experimental and density functional theory (DFT) calculations. This review systematically summarizes recent advances in ML-assisted OER research, focusing on key aspects including dataset construction, descriptor engineering, model development, and performance evaluation. Various ML techniques, ranging from traditional algorithms such as Random Forest and Support Vector Machines to advanced deep learning approaches, are critically discussed in the context of catalyst screening and activity prediction. Particular attention is given to the role of physicochemical descriptors, including adsorption energies and electronic structure parameters, in governing model performance and interpretability. Furthermore, this review highlights current challenges, such as data scarcity, lack of standardization, and limited model generalization, while discussing emerging trends including active learning, explainable AI, and integration with high-throughput simulations. By providing a comprehensive overview, this work aims to guide future research toward the development of robust, interpretable, and scalable ML frameworks for accelerating the discovery of efficient OER catalysts.
A comprehensive account of ML applications in NRR, covering curated experimental databases, feature engineering based on atomic, structural, and DFT‐derived descriptors, and ML‐guided insights into single‐atom, dual‐atom, alloy, oxide, nitride, and defect‐engineered catalysts are presented.
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
With growing concerns over climate change, electrochemical CO2 reduction (CO2RR) has attracted significant attention for converting CO2 into valuable products under mild conditions, but catalyst design remains challenging because traditional, experiment-driven approaches are inefficient and time-consuming. Recently, machine learning (ML), as an advanced data-driven technology, has been widely applied to catalyst design and performance prediction, providing new approaches for rapid screening, structural optimization, and mechanistic exploration of catalysts. Building on these recent advances, this review summarizes the latest applications of ML in CO2RR technology, focusing on ML applications in catalyst performance prediction, key descriptor discovery, catalyst screening, mechanistic investigation, and optimization of synthesis parameters. Additionally, this review discusses the challenges faced by ML in CO2RR catalyst development, including data scarcity, poor model generalization, and difficulties in multiobjective optimization, while looking ahead to future directions such as transfer learning, the establishment of automated synthesis platforms, and the integration of multiobjective optimization frameworks, thereby providing a roadmap for the rational design of next-generation CO2RR catalysts that aims to guide the development of data-driven catalyst design approach.
Photocatalytic water splitting has garnered immense interest as a sustainable pathway for clean hydrogen production by directly converting solar energy into chemical fuel. However, challenges related to intricate charge carrier dynamics, surface redox kinetics, and the vast search space for multicomponent catalysts continue to constrain the systematic development of efficient systems. Machine learning (ML) has emerged as a transformative tool to address these bottlenecks by enabling the accurate prediction of electronic properties, the identification of promising heterostructures, and the optimization of reaction conditions while reducing reliance on traditional trial‐and‐error methods. By capturing complex nonlinear correlations among structural descriptors and catalytic performance, ML facilitates the exploration of high‐dimensional design spaces that are essential for advancing solar‐to‐fuel conversion research. This review provides a comprehensive overview of how ML supports systematic materials innovation to realize stable and high‐efficiency systems for sustainable hydrogen evolution. As such, the integration of ML with experimental and theoretical methodologies is expected to establish a predictive and systematic framework for photocatalyst development, thereby accelerating progress toward scalable solar‐to‐hydrogen energy conversion.
Heesung Yoon, Jin Hyuk Cho, Wee‐Jun Ong et al.· ChemPhotoChem· 0 citations
The discovery and development of high-performance catalysts, which is crucial across all catalysis areas, requires advanced technologies and innovative approaches. Recently, machine learning (ML) has shown promise in accelerating this process, but its capability and examples of discovery of truly novel catalysts have remained limited. In this study, we describe an ML approach that goes beyond the traditional element pool, incorporating elements that have not been previously studied, to develop highly efficient catalysts for ethanol synthesis via CO2 hydrogenation. Starting with an initial data set of 58 catalysts (274 data points obtained at reaction temperatures ranging from 240-400 °C), we conducted 24 iterations of a closed-loop discovery system (ML predictions + experimental validation), testing a total of 555 catalysts (2477 data points), and building a large experimental data set. More than 50 catalysts with superior activity were discovered through this data-driven approach. The multielemental Pd(0.8)-Au(0.3)/K(2.5)-Sr(1)-Fe(20)-Zn(4)-Cd(2)-Yb(1)-Re(1)/CeO2(25%)-ZrO2 catalyst, where the numbers in parentheses represent weight percent (wt %), was identified as the most effective catalyst for ethanol synthesis (ethanol space-time yield: 8.2 mmol gcat-1 h-1 with a CO2 conversion of 57.6% and an ethanol selectivity of 23.2% under reaction conditions of 360 °C, 4 MPa, 12 L gcat-1 h-1, H2/CO2 = 3/1). Comprehensive characterizations, including in situ/operando techniques such as X-ray absorption spectroscopy (XAS), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), enable us to highlight the critical roles of each constituting element in improving ethanol synthesis efficiency.
Pengfei Du, Abdellah Ait El Fakir, S. Mine et al.· Journal of the American Chem...· 0 citations
Electricity‐driven water splitting presents significant potential for storing electrical energy in the form of hydrogen gas. However, its overall efficiency is limited by the oxygen evolution reaction (OER), a kinetically sluggish reaction. Overcoming this limitation requires the development of high‐performance, cost‐effective, and durable catalysts. However, traditional screening methods are time‐ and resource‐intensive due to the vast chemical space, hindering the development of OER electrocatalysts. Recently, machine learning (ML) has emerged as a powerful tool for accelerating materials discovery by uncovering structure‐property relationships. This review provides a comprehensive analysis of strategies for developing ML models tailored to various research perspectives in the design of OER electrocatalysts. We begin by outlining the key components of ML models, followed by an in‐depth discussion of the strategies for assembling ML frameworks with specific objectives in OER catalyst design. Subsequently, we classify recent advances in ML applications to OER electrocatalysis into three main areas: using ML to investigate the formability of OER electrocatalysts, studying the activity of OER electrocatalysts, and assist the characterization of OER electrocatalysts. Finally, we discuss the challenges of integrating ML into OER research and highlight future opportunities to utilize ML to revolutionize the development of OER electrocatalysts.
Lu Jia, Zihao Jin, Yueyang Tan et al.· Advanced Functional Material...· 0 citations
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