# Resolving Molecular Perturbations Near Undercoordinated Metals

## TL;DR

This paper introduces a method using SERS and machine learning to study how molecules interact with metal surfaces, helping understand catalytic processes at the molecular level.

## Contribution

A novel approach combining SERS and machine learning to resolve molecular perturbations near undercoordinated metal sites.

## Key findings

- Machine learning identifies metal-induced molecular perturbations via frequency wandering in vibrational energies.
- DFT modeling confirms the nature of interactions between molecules and undercoordinated metal sites.
- The method reveals how molecules deform during interactions with catalytically active metal surfaces.

## Abstract

Metal surfaces can act as efficient heterogeneous catalysts,
but
their underlying mechanisms are often poorly understood. This is due
to the highly transient nature of the underpinning interactions occurring
at the single-molecule level, making these difficult to resolve by
using traditional analysis techniques. Here, we present a methodology
to study metal–molecule interactions near undercoordinated
binding sites using single-molecule surface-enhanced Raman spectroscopy
(SERS). We demonstrate how machine learning can identify the metal-induced
molecular perturbations by recognizing concurrent frequency wandering
in vibrational energies, and we compare these peak displacements to
extensive DFT modeling to reveal what interactions are occurring.
This allows us to resolve how molecules are deformed as they interact
with binding sites on metal surfaces. The work provides rare insight
into the dynamics and behavior of molecules at catalytically active
interfaces and can aid in the rational design of heterogeneous catalysts.