Statistics makes a difference: machine learning adsorption dynamics of functionalized cyclooctyne on Si(001) at DFT accuracy

Abstract

The interpretation of experiments on reactive semiconductor surfaces requires statistically significant sampling of configurational space by molecular dynamics, but conventional ab initio methods are limited due to prohibitive computational costs. Machine-learning interatomic potentials provide a promising solution, bridging the gap between the chemical accuracy of short ab initio molecular dynamics (AIMD) and the extensive sampling required to simulate experiment. Using ethinyl-functionalized cyclooctyne adsorption on Si(001) as a model system, we demonstrate that conventional AIMD undersamples the configurational space, resulting in discrepancies with scanning tunnelling microscopy and X-ray photoelectron spectroscopy data. To resolve these inconsistencies, we employ pre-trained equivariant message-passing neural networks, fine-tuned on only a few thousand AIMD snapshots, and integrate them into a “molecular-gun” workflow. This approach generates 10 000 independent trajectories more than 1000 times faster than AIMD. These simulations recover rare intermediates, clarify the competition between adsorption motifs, and reproduce the experimentally dominant on-top [2 + 2] cycloaddition structure. Our results show that fine-tuning of pre-trained foundational models enables statistically converged, chemically accurate simulations of bond-forming and bond-breaking events on complex surfaces, providing a scalable route to reconcile atomistic theory with experimental ensemble measurements in semiconductor functionalization.