Monitoring C-C coupling in catalytic reactions via machine-learned infrared spectroscopy

Tracking atomic structural evolution along chemical transformation pathways is essential for optimizing chemical transitions and enhancing control. However, molecule-level knowledge of structural rearrangements during chemical processes remains a great challenge. Here, we couple infrared spectroscopy as a non-invasive method to probe molecular transformations, with a machine-learned protocol to immediately map the spectroscopic fingerprints to atomistic structures. From the theoretical perspective, we demonstrate it here with the example of C-C coupling in catalytic reactions, elucidating various structural conformations along dynamic trajectories. Within the transferable application to the specific CO-CO dimerization reaction, the structural and energetic variations of the critical chemical species could be identified via infrared spectroscopy. This approach extends the power of spectroscopy from fingerprinting chemical configurations to using them for assigning dynamic structural information.