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Machine learning assisted multifrequency AFM: Force model prediction

2023; American Institute of Physics; Volume: 123; Issue: 23 Linguagem: Inglês

10.1063/5.0176688

1520-8842

Lamiaa Sami Elsherbiny, Sérgio Santos, Karim Gadelrab, Tuza Adeyemi Olukan, J. Font, Víctor Barcons, Matteo Chiesa,

Advanced Electron Microscopy Techniques and Applications

Multifrequency atomic force microscopy (AFM) enhances resolving power, provides extra contrast channels, and is equipped with a formalism to quantify material properties pixel by pixel. On the other hand, multifrequency AFM lacks the ability to extract and examine the profile to validate a given force model while scanning. We propose exploiting data-driven algorithms, i.e., machine learning packages, to predict the optimum force model from the observables of multifrequency AFM pixel by pixel. This approach allows distinguishing between different phenomena and selecting a suitable force model directly from observables. We generate predictive models using simulation data. Finally, the formalism of multifrequency AFM can be employed to analytically recover material properties by inputting the right force model.