arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.04882cond-mat.mes-hallcond-mat.mtrl-sci
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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

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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