Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO<sub>2</sub> Reduction Performance Using Machine Learning Algorithms-Reference-Cited by-同舟云学术

Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO₂ Reduction Performance Using Machine Learning Algorithms

Published:2024-05-20 Issue:22 Volume:146 Page:15648-15658
ISSN:0002-7863
Container-title:Journal of the American Chemical Society
language:en
Short-container-title:J. Am. Chem. Soc.

Author:

Bonke Shannon A.¹^ORCID,Trezza Giovanni²^ORCID,Bergamasco Luca²,Song Hongwei³^ORCID,Rodríguez-Jiménez Santiago¹^ORCID,Hammarström Leif³^ORCID,Chiavazzo Eliodoro²^ORCID,Reisner Erwin¹^ORCID

Affiliation:

1. Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom

2. Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin 10129, Italy

3. Department of Chemistry, Ångström Laboratory, Uppsala University, Box 523, Uppsala 75120, Sweden

Funder

Horizon 2020 Framework Programme

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/jacs.4c01305

Reference49 articles.

1. Best practices for experiments and reporting in photocatalytic CO2 reduction

2. Photocatalytic CO2 reduction

3. Machine learning in catalysis

4. Bayesian reaction optimization as a tool for chemical synthesis

5. A mobile robotic chemist

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