Automated structure discovery for Tip Enhanced Raman Spectroscopy

TL;DR

This paper introduces a machine learning encoder-decoder model trained on simulated TERS images to automatically predict molecular structures, significantly reducing analysis time and computational effort in nanoscale spectroscopy.

Contribution

The study presents a novel deep learning approach for direct structure prediction from TERS images, bypassing traditional quantum-chemistry calculations.

Findings

High accuracy in structure prediction from simulated data

Demonstrates feasibility of automating TERS image analysis

Lays groundwork for applying methods to experimental datasets

Abstract

Tip-Enhanced Raman Spectroscopy (TERS) provides nanoscale chemical fingerprints alongside high-resolution topographic mapping of molecules, offering a powerful tool for materials discovery. However, TERS image datasets are challenging to interpret and typically demand time-consuming, computationally intensive quantum-chemistry calculations. To overcome this problem, we present an encoder-decoder model trained and evaluated on simulated TERS images of planar molecules, enabling direct prediction of molecular structures from spectral simulated data with high accuracy. Our approach demonstrates the feasibility of automating molecular structure identification from TERS images, bypassing traditional manual analysis. These findings provide a foundation for extending machine learning methods to experimental TERS datasets, potentially accelerating molecular discovery by integrating nanoscale spectroscopy with automated computational analysis.