Dresden 2026 – wissenschaftliches Programm

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AKPIK: Arbeitskreis Physik, moderne Informationstechnologie und Künstliche Intelligenz

AKPIK 5: Poster

AKPIK 5.11: Poster

Donnerstag, 12. März 2026, 15:00–16:30, P5

Machine Learning for Tip Enhanced Raman Spectroscopy — •Harshit Sethi, Orlando Silveira, and Adam Foster — Aalto University, Espoo, Finland

Tip Enhanced Raman Spectroscopy (TERS) provides nanoscale chemical fingerprint 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.

Keywords: machine learning; scanning probe microscopy; tip enhanced raman spectroscopy; imaging