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Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production
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DOI：10.3389/fchem.2026.1894425.png)
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Abstract
En 中文
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.
Keywords:
machine learning
high-throughput screening
water splitting
green hydrogen
electrocatalyst design
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