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Cross-material catalyst discovery via deep learning
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AI
DOI：10.1038/s41563-026-02622-6.png)
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Abstract
En 中文
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. A deep-learning framework unites knowledge from distinct catalyst families to predict an unexplored class of water-splitting catalysts, revealing an active single-atom catalyst that outperforms both the training and predicted material classes.
Keywords:
catalyst discovery
machine learning
deep learning
single-atom catalysts
perovskite oxides