Discovering CO Adsorption and Desorption Pathways from Chemical Reaction Neural Networks Modeling of Transient Kinetics Spectroscopy

We demonstrate a data-driven approach to interpret surface reactions by combining time-resolved gas-pulsing infrared spectroscopy with Chemical Reaction Neural Networks (CRNN). Using CO adsorption and desorption on Pd(111) at 460K-490K as a model system, we show how transient kinetic data can reveal detailed reaction mechanisms. Starting with a simple one-species model, we systematically evaluate increasingly complex mechanisms involving hollow- and bridge-site adsorption. Despite similar goodness of fit to the same experimental absorbance data, our models predict distinct coverage dynamics for different adsorption sites. Through analysis of spectral peak stability and predicted dynamics, we identify a mechanism where CO primarily adsorbs on bridge sites followed by rapid conversion to hollow sites as the most physically consistent with experimental observations. This work provides a framework for extracting mechanistic insights from limited experimental data, demonstrating how machine learning can bridge the gap between transient kinetic measurements and molecular-level understanding of surface reactions.