Abstract
The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e., conversion from detected signals to descriptors specific to tip-surface interactions and subsequently to material's properties. Here, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We explore the nonlinear mechanical behaviors spatially in a classical ferroelectric material, PbTiO3. We observe the non-trivial evolution of the Duffing stiffness term and the nonlinearity of the sample surface, determine spatial clustering of the nonlinear response, and perform a Landau analysis on predicting the nonlinear coefficient, which indicates that ferroelectric behavior can be a cause of the observed results. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls may be broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.
Original language | English |
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Article number | 054105 |
Journal | Journal of Applied Physics |
Volume | 128 |
Issue number | 5 |
DOIs | |
State | Published - Aug 7 2020 |
Funding
This research was conducted at the Center for Nanophase Materials Sciences, which also provided support (S.J., S.V.K., and R.K.V.) and is a U.S. DOE Office of Science User Facility. The PFM portion of this work was supported by the U.S. Department of Energy, Office of Science, Materials Sciences and Engineering Division (K.P.K.). This research used resources of the Compute and Data Environment for Science (CADES) at Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S Department of Energy under Contract No. DE-AC05-00OR22725.