Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules-Reference-Cited by-同舟云学术

Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules

Published:2021 Issue:32 Volume:12 Page:10742-10754
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Li Xiaobo¹^ORCID,Maffettone Phillip M.¹²,Che Yu¹³^ORCID,Liu Tao¹,Chen Linjiang¹³^ORCID,Cooper Andrew I.¹³^ORCID

Affiliation:

1. Department of Chemistry & Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool L7 3NY, UK

2. National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, New York 11973, USA

3. Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, 51 Oxford Street, Liverpool L7 3NY, UK

Abstract

We developed models to predict the photoactivity of organic molecules for photocatalytic hydrogen evolution by integrating experiment, computation, and machine learning. This marks a step toward the data-driven discovery of molecular photocatalysts.

Funder

Engineering and Physical Sciences Research Council

Leverhulme Trust

Brookhaven National Laboratory

Laboratory Directed Research and Development

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SC/D1SC02150H

Reference58 articles.