Local descriptors-based machine learning model refined by cluster analysis for accurately predicting adsorption energies on bimetallic alloys

Abstract

The CatBoost method, combined with cluster filtering, accurately predicts adsorption energies on metal alloys. The approach uses local chemical descriptors to understand chemisorption on metal alloys, which is essential for catalytic applications.

CatEmbed: A Machine-Learned Representation Obtained via Categorical Entity Embedding for Predicting Adsorption and Reaction Energies on Bimetallic Alloy Surfaces

“…Machine learning methods aimed at predicting adsorption energies on metallic surfaces at the DFT level of theory have emerged as a promising approach to improving the efficiency of highthroughput screening in catalysis. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] To this end, a variety of machine learning descriptors have been developed to predict adsorption energies. This includes basic elemental properties and electronic descriptors, such as atomic number, atomic radius, density, melting point, d-band representations, [16][17][18] and Pauling electronegativities.…”

CatEmbed: A Machine-Learned Representation Obtained via Categorical Entity Embedding for Predicting Adsorption and Reaction Energies on Bimetallic Alloy Surfaces

“…Machine learning methods aimed at predicting adsorption energies on metallic surfaces at the DFT level of theory have emerged as a promising approach to improving the efficiency of high-throughput screening in catalysis. − To this end, a variety of machine learning descriptors have been developed to predict adsorption energies. This includes basic elemental properties and electronic descriptors, such as atomic number, atomic radius, density, melting point, d-band representations, − and Pauling electronegativities.…”