# Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes

arXiv: https://arxiv.org/abs/2008.03226 · HTML: https://mlsanity.com/paper/2008.03226/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 14 of 14)

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- [SoDaDE: Solvent Data-Driven Embeddings with Small Transformer Models](https://mlsanity.com/paper/2509.22302/) (2025)
- [Sample Efficient Generative Optimization for Molecular Design](https://mlsanity.com/paper/2607.12488/) (2026)
- [Beyond Learning on Molecules by Weakly Supervising on Molecules](https://mlsanity.com/paper/2602.04696/) (2026)
- [Triply-Scalable Equivariant Gaussian Process Modeling](https://mlsanity.com/paper/2609.21085/) (2026)

## Abstract (arXiv)

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