📝 SummarXiv

Abstract

The interpretation of experiments on reactive semiconductor surfaces requires statistically significant sampling of 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 1,000 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 geometry. 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.