Abstract
SummaryNew 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.
Highlights
The ever-increasing need for energy and the clear environmental impact of fossil fuels in the 21st century are driving a rapid move to renewable energy sources (Furlan and Mortarino, 2018; Meinshausen et al, 2009; Wang et al, 2015)
This study demonstrates the power of machine learning and the potential of big data to accelerate discovery of next-generation photocatalysts
Models with the best performance on the test set were used to screen a large set of untested materials to identify a shortlist predicted to have optimal bandgaps and useful hydrogen evolution reaction (HER) activity
Summary
The ever-increasing need for energy and the clear environmental impact of fossil fuels in the 21st century are driving a rapid move to renewable energy sources (Furlan and Mortarino, 2018; Meinshausen et al, 2009; Wang et al, 2015). We describe a multistep, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H2 evolution activities of photocatalysts. These models can rapidly screen a large space (>10 million materials) to identify promising, non-toxic compounds as candidate water splitting photocatalysts.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
