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<p>Electrocatalysis is pivotal for sustainable energy, addressing global challenges through reactions like Oxygen Evolution Reaction (OER), Hydrogen Evolution Reaction (HER), Oxygen Reduction Reaction (ORR), and Carbon Dioxide Reduction Reaction (CO₂RR). However, traditional electrocatalyst discovery is bottlenecked by resource-intensive experimental screening and computational methods, exacerbated by the <b>material generalization problem</b>—where machine learning models fail on unseen materials or conditions due to <b>distribution shifts</b> (Seh ZW et al. Science 355(6321). (2017), (Hu J et al. Digit Discov 3(2):300. (2024)). This review systematically synthesizes advancements in ML and <b>domain adaptation</b> for electrocatalyst evaluation. We detail key ML paradigms, including supervised, unsupervised, and reinforcement learning, and discuss their application alongside specialized architectures such as deep learning and physics-informed ML, emphasizing their role in property prediction and materials exploration. (Wu H et al. J Mater Inf 5(2). (2025)), (Ding R, et al. Chem Soc Rev 53(23):11390–11461. (2024)). Crucially, we dissect DA strategies: feature-alignment, adversarial DA, transfer learning, instance-based methods, and physics-guided DA, highlighting their mechanisms to bridge the <b>simulation-to-experiment gap</b> and transfer knowledge across material families and experimental conditions (Hu J et al. Digit Discov 3(2):300. (2024)), (Long M et al. arXiv (Cornell Univ). (2015)). The review underscores the importance of high-throughput descriptor engineering and specialized evaluation metrics to assess DA performance and physical consistency. While ML has expedited the initial stages of catalyst discovery, enabling rapid property prediction and material exploration, generalization gaps and deployment challenges persist. We propose critical design principles for robust ML models—emphasizing interpretability, uncertainty quantification, and active learning—and outline future directions towards truly generalizable, physically consistent, and autonomously discovered electrocatalysts, thereby advancing the development of sustainable energy technologies (Seh ZW et al. Science 355(6321). (2017)), (Karniadakis GE et al. Nat Rev Phys 3(6): 422. (2021)), (Moon J et al. Nat Mater 23(1):108. (2024)).</p>

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Machine Learning and Domain Adaptation for Material Generalization in Electrocatalyst Evaluation: A Comprehensive Review

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Parmeshwar U. Laghane,
  • Bhaskar Sathe,
  • Pravin Yannawar

摘要

Electrocatalysis is pivotal for sustainable energy, addressing global challenges through reactions like Oxygen Evolution Reaction (OER), Hydrogen Evolution Reaction (HER), Oxygen Reduction Reaction (ORR), and Carbon Dioxide Reduction Reaction (CO₂RR). However, traditional electrocatalyst discovery is bottlenecked by resource-intensive experimental screening and computational methods, exacerbated by the material generalization problem—where machine learning models fail on unseen materials or conditions due to distribution shifts (Seh ZW et al. Science 355(6321). (2017), (Hu J et al. Digit Discov 3(2):300. (2024)). This review systematically synthesizes advancements in ML and domain adaptation for electrocatalyst evaluation. We detail key ML paradigms, including supervised, unsupervised, and reinforcement learning, and discuss their application alongside specialized architectures such as deep learning and physics-informed ML, emphasizing their role in property prediction and materials exploration. (Wu H et al. J Mater Inf 5(2). (2025)), (Ding R, et al. Chem Soc Rev 53(23):11390–11461. (2024)). Crucially, we dissect DA strategies: feature-alignment, adversarial DA, transfer learning, instance-based methods, and physics-guided DA, highlighting their mechanisms to bridge the simulation-to-experiment gap and transfer knowledge across material families and experimental conditions (Hu J et al. Digit Discov 3(2):300. (2024)), (Long M et al. arXiv (Cornell Univ). (2015)). The review underscores the importance of high-throughput descriptor engineering and specialized evaluation metrics to assess DA performance and physical consistency. While ML has expedited the initial stages of catalyst discovery, enabling rapid property prediction and material exploration, generalization gaps and deployment challenges persist. We propose critical design principles for robust ML models—emphasizing interpretability, uncertainty quantification, and active learning—and outline future directions towards truly generalizable, physically consistent, and autonomously discovered electrocatalysts, thereby advancing the development of sustainable energy technologies (Seh ZW et al. Science 355(6321). (2017)), (Karniadakis GE et al. Nat Rev Phys 3(6): 422. (2021)), (Moon J et al. Nat Mater 23(1):108. (2024)).