MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces

TL;DR

MAD-SURF is a machine learning interatomic potential designed for rapid and accurate simulation of molecular adsorption on coinage metal surfaces, significantly reducing computational costs compared to traditional methods.

Contribution

This work introduces MAD-SURF, a novel machine learning potential trained on diverse datasets, enabling fast and accurate modeling of molecular interactions on metal surfaces.

Findings

Achieves DFT-level accuracy in energies and forces

Successfully reproduces adsorption geometries across substrates

Demonstrates applicability to real experimental systems

Abstract

Predicting how organic molecules adsorb, assemble, and interact on metal surfaces is central to surface chemistry and molecular electronics, particularly in the context of interpreting high-resolution scanning probe microscopy. Yet, the application of first-principles simulations to interfaces is hampered by the computational cost for evaluating the electronic structure for the large number of atoms typically involved. We hereby present MAD-SURF, a machine learning interatomic potential specifically tailored for molecular adsorption on coinage metal surfaces. Trained on a broad dataset spanning diverse molecules, adsorption motifs, surfaces, molecular dynamics trajectories and non-covalent aggregates, MAD-SURF achieves accuracy comparable to the underlying DFT reference while enabling simulations orders of magnitude faster than density functional theory. The model reliably reproduces energies, forces and adsorption geometries across the three coinage metal substrates. We demonstrate its capabilities on experimentally characterized systems, including organic monolayers, polycyclic aggregates, flexible biomolecules and the long-range herringbone reconstruction of gold. By merging accuracy, speed, and generalizability, MAD-SURF offers a practical framework for accelerating atomistic simulations and advancing data-driven workflows in surface science.