Real-Time Reaction Pathway Mapping via Machine Learning-Enhanced Operando Spectroscopy

Combine advanced operando spectroscopy techniques (like IR or NMR) with machine learning models trained to recognize the spectral fingerprints of every species in the reaction mixture—including starting materials, catalysts, products, and short-lived intermediates. Train convolutional neural networks to deconvolute noisy, overlapping signals in real-time spectroscopic data streams. This approach aims to produce a real-time map of the entire catalytic cycle, enabling direct observation of mechanisms, detection of unexpected off-cycle species or alternative pathways, and providing concrete evidence to resolve mechanistic debates and design next-generation catalysts with precision.

References:

1. NMR investigations on catalysts and conformations in organo- and photocatalytic reactions, and characterization of electrolytes and supramolecular switchable container molecules. M. Fleischmann (2012).