Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes

Understand

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.