Predicting Segregation Energy in Single Atom Alloys Using Physics and Machine Learning.

ABSTRACT: Single atom alloys (SAAs) show great promise as catalysts for a wide variety of reactions due to their tunable properties, which can enhance the catalytic activity and selectivity. To design SAAs, it is imperative for the heterometal dopant to be stable on the surface as an active catalytic site. One main approach to probe SAA stability is to calculate surface segregation energy. Density functional theory (DFT) can be applied to investigate the surface segregation energy in SAAs. However, DFT is computationally expensive and time-consuming; hence, there is a need for accelerated frameworks to screen metal segregation for new SAA catalysts across combinations of metal hosts and dopants. To this end, we developed a model that predicts surface segregation energy using machine learning for a s

SUBMITTER: Salem M 

PROVIDER: S-EPMC8830057 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature