Data-driven discovery of molecular photoswitches with multioutput Gaussian processes. Issue 45 (10th November 2022)

• Record Type: Journal Article
• Authors: Griffiths, Ryan-Rhys; Greenfield, Jake L.; Thawani, Aditya R.; Jamasb, Arian R.; Moss, Henry B.; Bourached, Anthony; Jones, Penelope; McCorkindale, William; Aldrick, Alexander A.; Fuchter, Matthew J.; Lee, Alpha A.
• Is Part Of: Chemical science. Volume 13:Issue 45(2022)
• Page Start: 13541
• Page End: 13551
• Publication Date: 2022-11-10
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d2sc04306h
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : We present a data-driven discovery pipeline for molecular photoswitches through multitask learning with Gaussian processes. Through subsequent screening, we identify several motifs with separated and red-shifted electronic absorption bands. 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 .