ArticleProceedings of the National Academy of Sciences of the United States of America2025
AI-driven design of multiprincipal element alloys for optimal water splitting.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- AI-driven design of multiprincipal element alloys for optimal water splitting.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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5 authors.
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Abstract
Water splitting for hydrogen production is essential in advancing the hydrogen economy. Multiprincipal element alloys offer promising opportunities for optimizing this process, yet their vast compositional space and the presence of local minima pose significant challenges for experimental and AI-driven exploration. To overcome these challenges, an AI framework is developed by integrating Gaussian Process Regression with a configuration entropy-based acquisition function for screening and a design of experiments (DoE) for data-efficient overpotential mapping. Through Bayesian optimization across 16.2 million chemical compositions, this entropy-screened and DoE dataset-trained AI identifies Fe
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