Mapping Atomic-Scale Metal-Molecule Interactions: Salient Feature Extraction through Autoencoding of Vibrational Spectroscopy Data.

Atomic-scale features, such as step edges and adatoms, play key roles in metal-molecule interactions and are critically important in heterogeneous catalysis, molecular electronics, and sensing applications. However, the small size and often transient nature of atomic-scale structures make studying such interactions challenging. Here, by combining single-molecule surface-enhanced Raman spectroscopy with machine learning, spectra are extracted of perturbed molecules, revealing the formation dynamics of adatoms in gold and palladium metal surfaces. This provides unique insight into atomic-scale processes, allowing us to resolve where such metallic protrusions form and how they interact with nearby molecules. Our technique paves the way to tailor metal-molecule interactions on an atomic level and assists in rational heterogeneous catalyst design.