Using Machine Learning to Guess Photochemical Reaction Pathways - V. Basil Hans; Aaron Kunal Tatpati (International Journal of Photochemistry and Photochemical Research, 2025)
Photochemical reactions are crucial to many activities in the fields of energy conversion, environmental cleanup, and synthetic chemistry. However, predicting their causes and results effectively is still very hard since they entail excited electronic states, nonadiabatic transitions, and complicated potential energy surfaces. Machine learning (ML) has been a powerful technique to go along with classic quantum chemistry methods in the last few years. It offers better prediction capability and lower processing costs. This study investigates the amalgamation of machine learning algorithms with photochemical data to simulate and forecast reaction pathways under light irradiation. We use a carefully chosen set of known photochemical reactions, which includes transition states, absorption spectra, and reaction outcomes. Random forests, support vector machines, and graph neural networks are examples of supervised learning models that are trained on quantum-chemically derived descriptors. These descriptors include HOMO-LUMO gaps, oscillator strengths, and excited-state energies. Unupervised clustering methods are also used to sort response types and find new mechanistic patterns. Our results show that ML models can accurately forecast if a reaction will work and what intermediates are expected to generate, which is far better than baseline statistical methods. Also, interpretability methods show important electrical properties that affect photochemical behaviour. This work shows how useful data-driven methods can be in photochemistry and paves the way for faster discovery and smarter design of light-driven processes in materials science and synthetic chemistry.
Keywords: Machine Learning; Photochemistry; Predictive Modelling; excited states; quantum descriptors; reaction pathways