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arXiv · 2004.04882

Machine-learning Based Extraction of the Short-Range Part of the Interaction in Non-contact Atomic Force Microscopy

Zhuo Diao·Daiki Katsube·Hayato Yamashita·Yoshiaki Sugimoto·Oscar Custance·Masayuki Abe
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Abstract

A machine-learning method for extracting the short-range part of the probe-surface interaction from force spectroscopy curves is presented. Our machine-learning algorithm consists of two stages: the first stage determines a boundary that separates the region where the short-range interaction is dominantly acting on the probe, and a second stage that finds the parameters to fit the interaction over the long-range region. We successfully applied this method to force spectroscopy maps acquired over the Si(111)-(7x7) surface and found, as a result, a faint structure on the short-range interaction for one of the probes used in the experiments that would have probably been obviated using human-supervised fitting strategies.

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Zhuo Diao, Daiki Katsube, Hayato Yamashita, Yoshiaki Sugimoto, Oscar Custance, Masayuki Abe. 2020-04-10. Machine-learning Based Extraction of the Short-Range Part of the Interaction in Non-contact Atomic Force Microscopy. https://doi.org/10.1063/5.0007754

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