Group and Period-Based Representations for Improved Machine Learning Prediction of Heterogeneous Alloy Catalysts

Machine learning has recently emerged as an efficient and powerful alternative to density functional theory for studying heterogeneous catalysis. Machine learning methods rely on a geometrical representation of the chemical environment around the catalytic adsorption site based on physical or chemical descriptors. Here, we show that replacing the atomic number in geometrical representations with elemental groups and periods (GP) yields significant improvements in predicted adsorption energies on bimetallic alloy surfaces. Notably, the GP-based Labeled Site Crystal Graph representation reported here achieves mean absolute error (MAE) ∼0.05 eV (near chemical accuracy) in predicting hydrogen adsorption and MAE ∼0.10 eV for other strong binding adsorbates such as carbon, nitrogen, oxygen, and sulfur. We also show GP-based representations to be robust in predicting adsorption on surface facets, elements, and alloys that are not included in the initial training set. This reliability makes GP-based representations an ideal basis for high-throughput approaches and materials discovery based on active learning techniques, which often involve limited training sets.