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Meta model photocatalysts iScience.pdf (3.06 MB)
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Use of Meta Models for Rapid Discovery of Narrow Bandgap Oxide Photocatalysts

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journal contribution
posted on 20.09.2021, 06:25 by Haoxin Mai, Tu Le, Takashi Hisatomi, Dehong Chen, Kazunari Domen, David WinklerDavid Winkler, Rachel Caruso
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 evolution activities were consistent with the predictions. Conspicuously, the two compounds with strong photoactivities under UV and visible light are promising visible-light-driven water splitting photocatalysts. This study demonstrates the power of machine learning and the potential of big data to accelerate discovery of next-generation photocatalysts.

Funding

The Australian Research Council is acknowledged for support through a Discovery Project (DP180103815).

History

Publication Date

24/09/2021

Journal

iScience

Volume

24

Issue

9

Article Number

103068

Pagination

(p. 1-18)

Publisher

Cell Press

ISSN

2589-0042

Rights Statement

The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.

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