Berlin 2024 – wissenschaftliches Programm

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O: Fachverband Oberflächenphysik

O 103: Focus Session: Molecular Nanostructures on Surfaces: On-Surface Synthesis and Single-Molecule Manipulation IV

O 103.2: Vortrag

Freitag, 22. März 2024, 11:00–11:15, HE 101

Faster Single-Molecule Manipulations by STM Tunneling Current Analysis with Neural Networks — •Michael Obermayr, Bernhard Ramsauer, and Oliver T. Hofmann — Institute of Solid State Physics, NAWI Graz, Graz University of Technology, Petersgasse 16, 8010 Graz, Austria

Scanning tunneling microscopy (STM) allows precise manipulation of single atoms and molecules on surfaces. Recent AI-driven advancements in manipulating arbitrary molecules [1] open the door to automatic assembly of artificial nanostructures. A significant time portion is required for the imaging before and after each manipulation step, which often acts as the speed bottle neck. In this work we train a neural network on the tunneling current to predict the shift in position and orientation of an object, limiting the need for constant imaging.

The challenge in training a neural network lies in obtaining a suitable dataset containing all necessary inputs and outputs. Here we start with a measurement series of single-molecule manipulations paired with the respective precedent and subsequent images of the surface. The action outcome is extracted from these images with machine vision. Thus, a suitable training dataset for neural networks can be generated systematically, while ensuring broad applicability towards arbitrary molecules.

The fully trained network allows to track the manipulated molecule despite skipping a significant fraction of the imaging steps, which leads to an accelerated manipulation process.

[1] B. Ramsauer et al., J. Phys. Chem. A, 127, 2041 (2023)

Keywords: Scanning tunneling microscopy; Single-Molecule Manipulation; Nanostructures; Machine Learning; Machine Vision