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<p>The discovery of catalysts is typically confined within individual material classes, limiting insight from across material types. Here we demonstrate a machine learning approach that bridges catalyst families by identifying co-descriptors derived from two experimental datasets: single-atom catalysts (SACs) on carbon and bulk perovskite oxides. This co-descriptor set, selected through automated statistical and natural-language analyses, enabled integration of distinct experimental catalyst datasets by yielding shared activity-related chemical features. The resulting unified model, the crossbreeding neural network (CBNN), enables prediction of oxygen evolution activity in a previously untrained class—SACs on perovskite oxides. The CBNN precisely predicted performance trends of experimentally synthesized catalysts by overpotential, including a multimetallic catalyst with superior activity compared with all previous candidates. Explainable machine learning further connected descriptor importance and surface atomic contributions to activity trends. These results suggest that cross-material machine learning can accelerate the discovery of high-performance catalysts beyond known design spaces.</p>

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Cross-material catalyst discovery via deep learning

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Junseok Moon,
  • Seungwoo Yoo,
  • Jaehyuk Shim,
  • Sungeun Heo,
  • Jeong Hyun Kim,
  • Megalamane S. Bootharaju,
  • Kug-Seung Lee,
  • Jaeyune Ryu,
  • Yung-Eun Sung,
  • Taeghwan Hyeon

摘要

The discovery of catalysts is typically confined within individual material classes, limiting insight from across material types. Here we demonstrate a machine learning approach that bridges catalyst families by identifying co-descriptors derived from two experimental datasets: single-atom catalysts (SACs) on carbon and bulk perovskite oxides. This co-descriptor set, selected through automated statistical and natural-language analyses, enabled integration of distinct experimental catalyst datasets by yielding shared activity-related chemical features. The resulting unified model, the crossbreeding neural network (CBNN), enables prediction of oxygen evolution activity in a previously untrained class—SACs on perovskite oxides. The CBNN precisely predicted performance trends of experimentally synthesized catalysts by overpotential, including a multimetallic catalyst with superior activity compared with all previous candidates. Explainable machine learning further connected descriptor importance and surface atomic contributions to activity trends. These results suggest that cross-material machine learning can accelerate the discovery of high-performance catalysts beyond known design spaces.