Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions.

ABSTRACT: Low-cost, efficient catalyst high-throughput screening is crucial for future renewable energy technology. Interpretable machine learning is a powerful method for accelerating catalyst design by extracting physical meaning but faces huge challenges. This paper describes an interpretable descriptor model to unify activity and selectivity prediction for multiple electrocatalytic reactions (i.e., O₂/CO₂/N₂ reduction and O₂ evolution reactions), utilizing only easily accessible intrinsic properties. This descriptor, named ARSC, successfully decouples the atomic property (A), reactant (R), synergistic (S), and coordination effects (C) on the d-band shape of dual-atom sites, which is built upon our developed physically meaningful feature engineering and

SUBMITTER: Lin X 

PROVIDER: S-EPMC11408493 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature