{
  "id": "2004.04882",
  "version": "v1",
  "published": "2020-04-10T02:25:52.000Z",
  "updated": "2020-04-10T02:25:52.000Z",
  "title": "Machine-learning Based Extraction of the Short-Range Part of the Interaction in Non-contact Atomic Force Microscopy",
  "authors": [
    "Zhuo Diao",
    "Daiki Katsube",
    "Hayato Yamashita",
    "Yoshiaki Sugimoto",
    "Oscar Custance",
    "Masayuki Abe"
  ],
  "categories": [
    "cond-mat.mes-hall",
    "cond-mat.mtrl-sci"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-04-10T02:25:52.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "non-contact atomic force microscopy",
      "short-range part",
      "machine-learning",
      "extraction",
      "short-range interaction"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}