Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules
Abstract
Light-absorbing organic molecules are useful components in photocatalysts, but it is difficult to formulate reliable structure–property design rules. More than 100 million unique chemical compounds are documented in the PubChem database, and a significant sub-set of these are π-conjugated, light-absorbing molecules that might in principle act as photocatalysts. Nature has used natural selection to evolve photosynthetic assemblies; by contrast, our ability to navigate the enormous potential search space of organic photocatalysts in the laboratory is limited. Here, we integrate experiment, computation, and machine learning to address this challenge. A library of 572 aromatic organic molecules was assembled with diverse compositions and structures, selected on the basis of availability in our laboratory, rather than more sophisticated criteria. This training library was then assessed experimentally for sacrificial photocatalytic hydrogen evolution using a high-throughput, automated method. Quantum chemical calculations and machine learning were used to visualise, interpret, and ultimately to predict the photocatalytic activities of these molecules, covering a much broader chemical space than for previous polymer photocatalyst libraries. By applying unsupervised learning to the molecular structures, we identified structural features that were common in molecules with high catalytic activity. Further analysis using calculated molecular descriptors within a suite of supervised classification algorithmsmore »
- Authors:
-
- Department of Chemistry & Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool L7 3NY, UK
- Department of Chemistry & Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool L7 3NY, UK, National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, New York 11973, USA
- Department of Chemistry & Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool L7 3NY, UK, Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, 51 Oxford Street, Liverpool L7 3NY, UK
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Laboratory Directed Research and Development (LDRD) Program; Engineering and Physical Science Research Council (EPSRC)
- OSTI Identifier:
- 1798927
- Alternate Identifier(s):
- OSTI ID: 1809061
- Report Number(s):
- BNL-221836-2021-JAAM
Journal ID: ISSN 2041-6520; CSHCBM
- Grant/Contract Number:
- SC0012704; EP/N004884/1
- Resource Type:
- Published Article
- Journal Name:
- Chemical Science
- Additional Journal Information:
- Journal Name: Chemical Science Journal Volume: 12 Journal Issue: 32; Journal ID: ISSN 2041-6520
- Publisher:
- Royal Society of Chemistry (RSC)
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Li, Xiaobo, Maffettone, Phillip M., Che, Yu, Liu, Tao, Chen, Linjiang, and Cooper, Andrew I. Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules. United Kingdom: N. p., 2021.
Web. doi:10.1039/D1SC02150H.
Li, Xiaobo, Maffettone, Phillip M., Che, Yu, Liu, Tao, Chen, Linjiang, & Cooper, Andrew I. Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules. United Kingdom. https://doi.org/10.1039/D1SC02150H
Li, Xiaobo, Maffettone, Phillip M., Che, Yu, Liu, Tao, Chen, Linjiang, and Cooper, Andrew I. Wed .
"Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules". United Kingdom. https://doi.org/10.1039/D1SC02150H.
@article{osti_1798927,
title = {Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules},
author = {Li, Xiaobo and Maffettone, Phillip M. and Che, Yu and Liu, Tao and Chen, Linjiang and Cooper, Andrew I.},
abstractNote = {Light-absorbing organic molecules are useful components in photocatalysts, but it is difficult to formulate reliable structure–property design rules. More than 100 million unique chemical compounds are documented in the PubChem database, and a significant sub-set of these are π-conjugated, light-absorbing molecules that might in principle act as photocatalysts. Nature has used natural selection to evolve photosynthetic assemblies; by contrast, our ability to navigate the enormous potential search space of organic photocatalysts in the laboratory is limited. Here, we integrate experiment, computation, and machine learning to address this challenge. A library of 572 aromatic organic molecules was assembled with diverse compositions and structures, selected on the basis of availability in our laboratory, rather than more sophisticated criteria. This training library was then assessed experimentally for sacrificial photocatalytic hydrogen evolution using a high-throughput, automated method. Quantum chemical calculations and machine learning were used to visualise, interpret, and ultimately to predict the photocatalytic activities of these molecules, covering a much broader chemical space than for previous polymer photocatalyst libraries. By applying unsupervised learning to the molecular structures, we identified structural features that were common in molecules with high catalytic activity. Further analysis using calculated molecular descriptors within a suite of supervised classification algorithms revealed that light absorption, exciton electron affinity, electron affinity, exciton binding energy, and singlet–triplet energy gap had correlations with the photocatalytic performance. These trained predictive models can be used in future studies as filters to deprioritise or discard would-be low-activity candidate molecules from experiments, and to prioritize more favourable candidates. As a demonstration, we used virtual in silico experiments to show that it was possible to halve the experimental cost of finding 50% of the most active photocatalysts by using the machine learning model as an experimental advisor. We further showed that the ML advisor trained on the 572-molecule library could be used to make predictions for an unseen set of 96 molecules, achieving equivalent predictive accuracies to those in the initial training set. This marks a step toward the machine-learning assisted discovery of molecular organic photocatalysts and the approach might also be applied to problems beyond photocatalytic hydrogen evolution, such as CO2 reduction and photoredox chemistry.},
doi = {10.1039/D1SC02150H},
journal = {Chemical Science},
number = 32,
volume = 12,
place = {United Kingdom},
year = {Wed Aug 18 00:00:00 EDT 2021},
month = {Wed Aug 18 00:00:00 EDT 2021}
}
https://doi.org/10.1039/D1SC02150H
Works referenced in this record:
Photosensitized singlet oxygen and its applications
journal, November 2002
- DeRosa, M.
- Coordination Chemistry Reviews, Vol. 233-234
Organic Photoredox Catalysis
journal, June 2016
- Romero, Nathan A.; Nicewicz, David A.
- Chemical Reviews, Vol. 116, Issue 17
A Laser Flash Photolysis Study of Pyrene-1-Aldehyde. Intersystem Crossing Efficiency, Photoreactivity and Triplet State Properties in Various Solvents
journal, August 1983
- Kumar, Ch. V.; Chattopadhyay, S. K.; Das, P. K.
- Photochemistry and Photobiology, Vol. 38, Issue 2
Photochemical Mechanisms Responsible for the Versatile Application of Naphthalimides and Naphthaldiimides in Biological Systems
journal, December 1997
- Aveline, Béatrice M.; Matsugo, Seiichi; Redmond, Robert W.
- Journal of the American Chemical Society, Vol. 119, Issue 49
Shining Light on Carbon Nitrides: Leveraging Temperature To Understand Optical Gap Variations
journal, June 2018
- Li, Xiaobo; Melissen, Sigismund T. A. G.; Le Bahers, Tangui
- Chemistry of Materials, Vol. 30, Issue 13
Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
journal, January 2019
- Zahrt, Andrew F.; Henle, Jeremy J.; Rose, Brennan T.
- Science, Vol. 363, Issue 6424
Proflavin Catalyzed Photoproduction of Hydrogen from Organic Compounds
journal, February 1979
- Krasna, Alvin I.
- Photochemistry and Photobiology, Vol. 29, Issue 2
On representing chemical environments
journal, May 2013
- Bartók, Albert P.; Kondor, Risi; Csányi, Gábor
- Physical Review B, Vol. 87, Issue 18
Quantum Yield of Singlet Oxygen Production by Xanthene Derivatives
journal, March 1983
- Gandin, E.; Lion, Y.; Van de Vorst, A.
- Photochemistry and Photobiology, Vol. 37, Issue 3
Current understanding and challenges of solar-driven hydrogen generation using polymeric photocatalysts
journal, September 2019
- Wang, Yiou; Vogel, Anastasia; Sachs, Michael
- Nature Energy, Vol. 4, Issue 9
QSAR Modeling: Where Have You Been? Where Are You Going To?
journal, January 2014
- Cherkasov, Artem; Muratov, Eugene N.; Fourches, Denis
- Journal of Medicinal Chemistry, Vol. 57, Issue 12
Design of BODIPY Dyes as Photosensitisers in Multicomponent Catalyst Systems for Light-Driven Hydrogen Production
journal, August 2015
- Dura, Laura; Ahrens, Johannes; Pohl, Marga-Martina
- Chemistry - A European Journal, Vol. 21, Issue 39
Enhanced photocatalytic hydrogen evolution from organic semiconductor heterojunction nanoparticles
journal, February 2020
- Kosco, Jan; Bidwell, Matthew; Cha, Hyojung
- Nature Materials, Vol. 19, Issue 5
Structure–Property Relationships for Tailoring Phenoxazines as Reducing Photoredox Catalysts
journal, March 2018
- McCarthy, Blaine G.; Pearson, Ryan M.; Lim, Chern-Hooi
- Journal of the American Chemical Society, Vol. 140, Issue 15
Triplet photosensitizers: from molecular design to applications
journal, January 2013
- Zhao, Jianzhang; Wu, Wanhua; Sun, Jifu
- Chemical Society Reviews, Vol. 42, Issue 12
Room-temperature phosphorescence from organic aggregates
journal, August 2020
- Zhao, Weijun; He, Zikai; Tang, Ben Zhong
- Nature Reviews Materials, Vol. 5, Issue 12
Xanthene dyes as Sensitizers for the Photoreduction of Water
journal, May 1985
- Mau, A. W. -H.; Johansen, O.; Sasse, W. H. F.
- Photochemistry and Photobiology, Vol. 41, Issue 5
A metal-free polymeric photocatalyst for hydrogen production from water under visible light
journal, November 2008
- Wang, Xinchen; Maeda, Kazuhiko; Thomas, Arne
- Nature Materials, Vol. 8, Issue 1
Making Hydrogen from Water Using a Homogeneous System Without Noble Metals
journal, July 2009
- Lazarides, Theodore; McCormick, Theresa; Du, Pingwu
- Journal of the American Chemical Society, Vol. 131, Issue 26
Hydrogen Production by Molecular Photocatalysis
journal, October 2007
- Esswein, Arthur J.; Nocera, Daniel G.
- Chemical Reviews, Vol. 107, Issue 10
Virtual Excited State Reference for the Discovery of Electronic Materials Database: An Open-Access Resource for Ground and Excited State Properties of Organic Molecules
journal, October 2019
- Abreha, Biruk G.; Agarwal, Snigdha; Foster, Ian
- The Journal of Physical Chemistry Letters, Vol. 10, Issue 21
Predicting reaction performance in C–N cross-coupling using machine learning
journal, February 2018
- Ahneman, Derek T.; Estrada, Jesús G.; Lin, Shishi
- Science, Vol. 360, Issue 6385
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
journal, December 2019
- Toyao, Takashi; Maeno, Zen; Takakusagi, Satoru
- ACS Catalysis, Vol. 10, Issue 3
Efficient Photocatalytic Hydrogen Evolution from Water without an Electron Mediator over Pt−Rose Bengal Catalysts
journal, January 2009
- Zhang, Xiaojie; Jin, Zhiliang; Li, Yuexiang
- The Journal of Physical Chemistry C, Vol. 113, Issue 6
Photoredox Catalysis in Organic Chemistry
journal, June 2016
- Shaw, Megan H.; Twilton, Jack; MacMillan, David W. C.
- The Journal of Organic Chemistry, Vol. 81, Issue 16
Predicting the state of charge and health of batteries using data-driven machine learning
journal, March 2020
- Ng, Man-Fai; Zhao, Jin; Yan, Qingyu
- Nature Machine Intelligence, Vol. 2, Issue 3
Comparing molecules and solids across structural and alchemical space
journal, January 2016
- De, Sandip; Bartók, Albert P.; Csányi, Gábor
- Physical Chemistry Chemical Physics, Vol. 18, Issue 20
Discovery and characterization of an acridine radical photoreductant
journal, April 2020
- MacKenzie, Ian A.; Wang, Leifeng; Onuska, Nicholas P. R.
- Nature, Vol. 580, Issue 7801
Carbon dots as photosensitisers for solar-driven catalysis
journal, January 2017
- Hutton, Georgina A. M.; Martindale, Benjamin C. M.; Reisner, Erwin
- Chemical Society Reviews, Vol. 46, Issue 20
Highly efficient organic light-emitting diodes from delayed fluorescence
journal, December 2012
- Uoyama, Hiroki; Goushi, Kenichi; Shizu, Katsuyuki
- Nature, Vol. 492, Issue 7428
A mobile robotic chemist
journal, July 2020
- Burger, Benjamin; Maffettone, Phillip M.; Gusev, Vladimir V.
- Nature, Vol. 583, Issue 7815
Multiwfn: A multifunctional wavefunction analyzer
journal, December 2011
- Lu, Tian; Chen, Feiwu
- Journal of Computational Chemistry, Vol. 33, Issue 5
Accelerated Discovery of Organic Polymer Photocatalysts for Hydrogen Evolution from Water through the Integration of Experiment and Theory
journal, May 2019
- Bai, Yang; Wilbraham, Liam; Slater, Benjamin J.
- Journal of the American Chemical Society, Vol. 141, Issue 22
Data-driven design of metal–organic frameworks for wet flue gas CO2 capture
journal, December 2019
- Boyd, Peter G.; Chidambaram, Arunraj; García-Díez, Enrique
- Nature, Vol. 576, Issue 7786
Heavy atom-free Keto-di-coumarin as earth-abundant strong visible light-harvesting photosensitizer for efficient photocatalytic hydrogen evolution
journal, July 2019
- Dong, Ru; Chen, Kai-Kai; Wang, Ping
- Dyes and Pigments, Vol. 166
Recent Advances in Conjugated Polymers for Visible‐Light‐Driven Water Splitting
journal, June 2020
- Zhao, Chengxiao; Chen, Zupeng; Shi, Run
- Advanced Materials, Vol. 32, Issue 28
Molecular systems for light driven hydrogen production
journal, January 2012
- Eckenhoff, William T.; Eisenberg, Richard
- Dalton Transactions, Vol. 41, Issue 42
Selective and Efficient Photocatalytic CO 2 Reduction to CO Using Visible Light and an Iron-Based Homogeneous Catalyst
journal, November 2014
- Bonin, Julien; Robert, Marc; Routier, Mathilde
- Journal of the American Chemical Society, Vol. 136, Issue 48
Water Oxidation with Cobalt‐Loaded Linear Conjugated Polymer Photocatalysts
journal, August 2020
- Sprick, Reiner Sebastian; Chen, Zheng; Cowan, Alexander J.
- Angewandte Chemie International Edition, Vol. 59, Issue 42
Machine Learning for Accelerated Discovery of Solar Photocatalysts
journal, October 2019
- Masood, Hassan; Toe, Cui Ying; Teoh, Wey Yang
- ACS Catalysis, Vol. 9, Issue 12
Metal-free photocatalysts for hydrogen evolution
journal, January 2020
- Rahman, Mohammad Ziaur; Kibria, Md Golam; Mullins, Charles Buddie
- Chemical Society Reviews, Vol. 49, Issue 6
Autonomous discovery in the chemical sciences part II: Outlook
journal, September 2019
- Coley, Connor W.; Eyke, Natalie S.; Jensen, Klavs F.
- Angewandte Chemie International Edition
Production of Hydrogen Peroxide by Photocatalytic Processes
journal, May 2020
- Hou, Huilin; Zeng, Xiangkang; Zhang, Xiwang
- Angewandte Chemie International Edition, Vol. 59, Issue 40
Light-induced polymer and polymerization reactions XXXIII: direct photoinitiation of methyl methacrylate polymerization by excited states of ketones
journal, February 1989
- Timpe, H. -J.; Kronfeld, K. -P.
- Journal of Photochemistry and Photobiology A: Chemistry, Vol. 46, Issue 2
Autonomous discovery in the chemical sciences part I: Progress
journal, September 2019
- Jensen, Klavs F.; Coley, Connor W.; Eyke, Natalie S.
- Angewandte Chemie International Edition
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
journal, June 2020
- Jablonka, Kevin Maik; Ongari, Daniele; Moosavi, Seyed Mohamad
- Chemical Reviews, Vol. 120, Issue 16
Thermally activated delayed fluorescence (TADF) dyes as efficient organic photosensitizers for photocatalytic water reduction
journal, January 2019
- Yu, Zhe-Jian; Lou, Wen-Ya; Junge, Henrik
- Catalysis Communications, Vol. 119
Recent advances in organic thermally activated delayed fluorescence materials
journal, January 2017
- Yang, Zhiyong; Mao, Zhu; Xie, Zongliang
- Chemical Society Reviews, Vol. 46, Issue 3
Combining theory and experiment in electrocatalysis: Insights into materials design
journal, January 2017
- Seh, Zhi Wei; Kibsgaard, Jakob; Dickens, Colin F.
- Science, Vol. 355, Issue 6321
Hydrogen evolution from the photolysis of alcoholic benzophenone solutions via redox catalysis
journal, December 1979
- Graetzel, Carole K.; Graetzel, Michael
- Journal of the American Chemical Society, Vol. 101, Issue 26
Porous, Fluorescent, Covalent Triazine-Based Frameworks Via Room-Temperature and Microwave-Assisted Synthesis
journal, April 2012
- Ren, Shijie; Bojdys, Michael J.; Dawson, Robert
- Advanced Materials, Vol. 24, Issue 17
Quantum chemistry structures and properties of 134 kilo molecules
journal, August 2014
- Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
- Scientific Data, Vol. 1, Issue 1
Machine learning for molecular and materials science
journal, July 2018
- Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh
- Nature, Vol. 559, Issue 7715
Robust and synthesizable photocatalysts for CO2 reduction: a data-driven materials discovery
journal, January 2019
- Singh, Arunima K.; Montoya, Joseph H.; Gregoire, John M.
- Nature Communications, Vol. 10, Issue 1