Figure 1. Machine learning model for predicting a new catalyst class by integrating distinct catalyst families (IMAGE)

Institute for Basic Science

Figure 1. Machine learning model for predicting a new catalyst class by integrating distinct catalyst families

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The researchers developed a machine learning model that integrates data from carbon-supported single-atom catalysts and perovskite oxide catalysts. The model predicts the activity of a new material class that was not included in training: single-atom catalysts supported on perovskite oxides. The surface atomic arrangement of the catalyst is learned as image information, while the bulk structure of the oxide is learned as graph information. By combining surface-design knowledge from single-atom catalysts with bulk-structure knowledge from perovskite oxides, the model predicts the overpotential of catalysts for the alkaline oxygen evolution reaction.

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Institute for Basic Science

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