论文标题
非接触原子力显微镜中相互作用的短距离部分的基于机器学习的提取
Machine-learning Based Extraction of the Short-Range Part of the Interaction in Non-contact Atomic Force Microscopy
论文作者
论文摘要
提出了一种用于从力光谱曲线提取探针表面相互作用的短距离部分的机器学习方法。我们的机器学习算法由两个阶段组成：第一阶段确定一个边界，该边界将短距离相互作用主要作用在探针上，第二阶段，第二阶段找到了在远距离区域上适合相互作用的参数。我们成功地应用了这种方法来强制在Si（111） - （7x7）表面上获得的光谱图，结果发现，对于实验中使用的一种探针，在短距离相互作用上具有微弱的结构，可能会使用人类抑制拟合策略来消除该探针。
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.
