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.
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
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.
Swetarekha Ram, Shalini Tomar, S. Bhattacharjee· Chemical Communications· 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
Developing efficient and durable non‐precious metal electrocatalysts for electrochemical water splitting remains a critical barrier to sustainable hydrogen production. Among earth‐abundant candidates, vanadium‐based oxide electrocatalysts are highly attractive due to their wide range of oxidation states (V
2+–
V
5+
), composition‐dependent tunability of active sites, and intrinsically flexible atomic structures. This review offers a comprehensive and mechanistic analysis of the six principal modification strategies: lattice engineering, heteroatom doping, heterojunction and interface engineering, carbon‐based hybridization, morphology engineering, and surface reconstruction and pre‐catalyst design. It highlights structure–property relationships, the identification of active sites, and operative oxygen evolution pathways. A distinctive finding across the strategies reviewed is that vanadium dissolution and surface reconstruction are design features, not degradation processes; thus, the as‐synthesized material frequently functions as a pre‐catalyst engineered to reconstruct under electrochemical operating conditions. This review further highlights how machine learning methods accelerate the atomistic modeling of structurally analogous oxide systems, offering an emerging simulation framework for addressing mechanistic questions that conventional first‐principles calculations cannot address at the scale required for this materials class. Finally, a personal perspective is offered on the challenges and future research directions for advancing vanadium‐based oxide electrocatalysts toward industrially relevant water‐splitting performance.
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.
Anion exchange membrane water electrolyzers (AEMWEs) are promising for hydrogen production, yet their performance is bottlenecked by the alkaline hydrogen evolution reaction (HER) with sluggish kinetics induced by high water dissociation barriers and imbalanced H*/OH* adsorption-desorption. Herein, interpretable machine learning (ML) is exploited as a core tool for precise catalyst structure optimization, guiding the fabrication of a Ru2Ni3-carbon nanotubes (CNTs) hybrid catalyst. The ML-engineered catalyst exhibits high HER activity, with an ultra-low overpotential of 14 mV at 10 mA cm–2 and a Tafel slope of 34.3 mV dec–1. When integrated into an AEMWE with a NiFe-LDH anode, the system achieves 1.86 V at 1 A cm–2 (80 °C, no iR correction) and maintains stability for 200 h. Experimental and theoretical studies confirm that the ML-tailored Ru2Ni3-CNTs synergy modulates d-band centers, reduces reaction barriers, and optimizes intermediate adsorption, highlighting ML’s pivotal role in rational electrocatalyst design for advanced AEMWEs.
