Predicting Activation Energy of Hydrocarbon Dehydrogenation on Au(111) via Machine Learning

Activation of C–H bonds in hydrocarbons is fundamental
for
synthesizing organic functional materials, yet traditional density
functional theory (DFT) methods for determining reaction energy barriers
are computationally intensive and often suffer from convergence challenges.
We report here the construction of a comprehensive DFT-based data
set of hydrocarbon dehydrogenation reactions on the Au(111) surface
and propose a feature-enhanced graph neural network (F-GNN) that integrates
eight chemically informed descriptors with molecular graph representations.
This F-GNN model accurately predicts reaction activation energies,
outperforming conventional approaches such as the Brønsted–Evans–Polanyi
relationship and standalone machine learning models. Our findings
demonstrate that combining chemical prior knowledge with data-driven
features enables efficient and precise energy barrier prediction,
offering a promising strategy to accelerate reaction path screening
and mechanistic understanding in surface-catalyzed hydrocarbon transformations.