  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
