Issue 45, 2022

Data-driven discovery of molecular photoswitches with multioutput Gaussian processes

Abstract

Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications. Engineering these properties into a system through synthetic design however, remains a challenge. Here, we present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning with Gaussian processes. In the prediction of electronic transition wavelengths, we demonstrate that a multioutput Gaussian process (MOGP) trained using labels from four photoswitch transition wavelengths yields the strongest predictive performance relative to single-task models as well as operationally outperforming time-dependent density functional theory (TD-DFT) in terms of the wall-clock time for prediction. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules. Through this screen, we identified several motifs that displayed separated electronic absorption bands of their isomers, exhibited red-shifted absorptions, and are suited for information transfer and photopharmacological applications. Our curated dataset, code, as well as all models are made available at https://github.com/Ryan-Rhys/The-Photoswitch-Dataset.

Graphical abstract: Data-driven discovery of molecular photoswitches with multioutput Gaussian processes

Supplementary files

Article information

Article type
Edge Article
Submitted
12 ago 2022
Accepted
16 set 2022
First published
10 nov 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2022,13, 13541-13551

Data-driven discovery of molecular photoswitches with multioutput Gaussian processes

R. Griffiths, J. L. Greenfield, A. R. Thawani, A. R. Jamasb, H. B. Moss, A. Bourached, P. Jones, W. McCorkindale, A. A. Aldrick, M. J. Fuchter and A. A. Lee, Chem. Sci., 2022, 13, 13541 DOI: 10.1039/D2SC04306H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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