This PhD project at the University of York's Department of Chemistry focuses on developing machine learning (ML) methods to predict the primary photoproducts of aromatic molecules following light-induced chemical changes. The research addresses a major challenge in photochemistry: the difficulty of predicting molecular excited states and their decay pathways, especially the identity of photoproducts, which are crucial in fields such as photocatalysis, organic electronics, and photomedicine. The project will use a combination of experimental and computational approaches. First, a training set of molecule-photoproduct data will be acquired by performing UV laser-LED photodissociation of aromatic molecules using advanced light-interfaced mass spectrometry.

Molecules will be mass-selected and isolated as gas-phase ions in an ion-trap, then photon-excited, with photoproducts identified via mass spectrometry. High-throughput measurements will be enabled by LED light sources, ensuring a uniform dataset ideal for ML training. A second training set will be generated from solution-phase photolysis experiments, with mass spectrometry used to identify photoproducts. These datasets will be used to develop ML protocols, employing neural networks and Python-based tools, to link organic molecules with their primary photoproducts and create predictive models for both gas-phase and solution-phase reactions.

The project is novel in its use of laser-interfaced mass spectrometry and aims to bridge the current gap in predictive tools for photoproducts, enabling predictions for molecules not previously analyzed and supporting photo-accelerated synthesis. Students will receive comprehensive training in data science, artificial intelligence, mass spectrometry, and experimental techniques, and will be encouraged to participate in conferences and international collaborations. The department supports equality and diversity, with initiatives to increase participation from under-represented groups.

Funding covers tuition, stipend, and research costs, with opportunities for both home and international students. Applicants should have or expect to achieve at least a UK upper second class degree in Chemistry or a related subject, and be interested in both experimental and computational research.

The application deadline is 6 January 2026, with interviews held in February. For more information, candidates can consult the university and department websites or contact the supervisor.