Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts.

ABSTRACT: New photocatalysts are traditionally identified through trial-and-error methods. Machine learning has shown considerable promise for improving the efficiency of photocatalyst discovery from a large potential pool. Here, we describe a multi-step, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H₂ evolution activities of photocatalysts. Trained on small datasets, these models can rapidly screen a large space (>10 million materials) to identify promising, non-toxic compounds as candidate water splitting photocatalysts. Two effective compounds and two controls possessing optimal bandgap values (∼2 eV) but not photoactivity as predicted by the models were synthesized. Their experimentally measured bandgaps and H₂

SUBMITTER: Mai H 

PROVIDER: S-EPMC8455646 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature