Specifying primary factors in the catalytic activity of metal clusters based on quantum chemical calculations and machine learning

Abstract

Because catalytic activities of metal nano clusters depend on the composition, size, environment, and structural isomers of the cluster, it has been difficult to elucidate the primary factors in their catalytic activities. In this study, we attempted to extract the factors in the catalytic activity using the sparse modeling techniques and the systematic quantum chemical calculations assisted by the automatic search of reaction pathways. In particular, the transition state energies for NO dissociation on Cu13 clusters were modeled with the orbital energies and local indices by the LASSO, SCAD, and MC+ regressions. It was found that the transition state energy negatively correlates with the LUMO energy. The SCAD and MC+ regressions could generate more compact and better models with higher correlation factors than the LASSO regression.