Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility.
Geun Ho GuMiriam LeeYousung JungDionisios G VlachosPublished in: Nature communications (2022)
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 ~10 8 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 ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.
Keyphrases
- transition metal
- machine learning
- highly efficient
- big data
- high throughput
- human health
- density functional theory
- molecular dynamics
- deep learning
- health risk
- small molecule
- molecular dynamics simulations
- quantum dots
- single cell
- genome wide
- artificial intelligence
- body composition
- molecular docking
- drug discovery
- electronic health record
- staphylococcus aureus
- resistance training
- cystic fibrosis
- gene expression
- dna methylation
- heavy metals
- mass spectrometry
- drinking water
- data analysis
- psychometric properties