Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility.

ABSTRACT: Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10⁸ potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethyle

SUBMITTER: Gu GH 

PROVIDER: S-EPMC9043206 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature