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

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