Unravelling the Kinetic Model of Photochemical Reactions via Deep Learning

Time-resolved spectroscopies have been playing an essential role in the elucidation of the fundamental mechanisms of light-driven processes, particularly in exploring relaxation models for electronically excited molecules. However, the determination of such models from experimentally obtained time-resolved and spectrally resolved data still demands a high degree of intuition, frequently poses numerical challenges, and is often not free from ambiguities. Here, we demonstrate the analysis of time-resolved laser spectroscopy data via a deep learning network to obtain the correct relaxation kinetic model. In its current design, the presented Deep Spectroscopy Kinetic Analysis Network (DeepSKAN) can predict kinetic models (involved states and relaxation pathways) consisting of up to five states, which results in 103 possible different classes, by estimating the probability of occurrence of a given kinetic model class. DeepSKAN was trained with synthetic time-resolved spectra spanning over 4 orders of magnitude in time with a unitless time axis, thereby demonstrating its potential as a universal approach for analyzing data from various time-resolved spectroscopy techniques in different time ranges. By adding the probabilities of each pathway of the top-k models normalized by the total probability, we can determine the relaxation pathways for a given data set with high certainty (up to 99%). Due to its architecture and training, DeepSKAN is robust against experimental noise and typical preanalysis errors like time-zero corrections. Application of DeepSKAN to experimental data is successfully demonstrated for three different photoinduced processes: transient absorption of the retinal isomerization, transient IR spectroscopy of the relaxation of the photoactivated DRONPA, and transient absorption of the dynamics in lycopene. This approach delivers kinetic models and could be a unifying asset in several areas of spectroscopy.