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Cross-material catalyst discovery via deep learning
Junseok Moon1,2, Seungwoo Yoo1,2, Jaehyuk Shim1,2
1Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea.
Nature Materials
|May 28, 2026
Summary
This study introduces a machine learning model that integrates diverse catalyst data, enabling accurate predictions for new catalyst types like single-atom catalysts on perovskite oxides.
Area of Science:
- Materials Science
- Catalysis
- Machine Learning
Background:
- Catalyst discovery is often limited to specific material classes, hindering cross-disciplinary insights.
- Integrating data from different catalyst types (e.g., single-atom catalysts and perovskite oxides) is challenging.
Purpose of the Study:
- To develop a machine learning approach that bridges different catalyst families.
- To enable the prediction of catalytic activity in previously untrained material classes.
Main Methods:
- Utilized co-descriptors derived from single-atom catalysts (SACs) on carbon and bulk perovskite oxide datasets.
- Employed automated statistical and natural-language analyses to select co-descriptors.
- Developed a unified crossbreeding neural network (CBNN) model.
Main Results:
- The CBNN successfully integrated distinct experimental catalyst datasets by identifying shared activity-related chemical features.
- The model accurately predicted oxygen evolution activity for single-atom catalysts on perovskite oxides, a previously untrained class.
- Identified a multimetallic catalyst with superior performance and utilized explainable AI to link descriptors to activity.
Conclusions:
- Cross-material machine learning accelerates catalyst discovery beyond existing design spaces.
- The CBNN approach facilitates the identification of high-performance catalysts by leveraging diverse data.
- Explainable AI provides insights into the atomic contributions driving catalyst performance.
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