Machine Learning in Understanding the Initial Interaction of the Atomic Force Microscope Probe with the Surface

Reliable determination of the tip-surface contact in atomic force microscopy measurements is necessary for structural and physico-mechanical analysis of the surface properties. Transition from non-contact interaction to contact one during experiments in air environment is accompanied by a rapid jump of the tip to the surface. High velocity of the tip movement in the area of non-contact interaction and the relatively low rate of the atomic force microscopy data capture do not allow determining of the onset of contact from the points of the force curve especially in the case of a deformable surface. The proposed solution is to use machine learning algorithms trained on model results. The interaction of the tip with the surface was modeled using a harmonic oscillator varying parameters of the probe, the material, and the experiment. As a result deflection of the probe in the moment of contact is predicted using input experimental parameters. Use of the developed algorithms is demonstrated by treating the results of the indentation of polyethylene. The obtained contact deflections are significantly differ from the available points of the experimental curves.

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