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  3. Cross-material Catalyst Discovery Via Deep Learning.
  1. Home
  3. Cross-material Catalyst Discovery Via Deep Learning.

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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

View abstract on PubMed

Summary
This summary is machine-generated.

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.

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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.