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Title: Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

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. In this work, 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 maymore » be broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [1]
+ Show Author Affiliations
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
  2. National Academy of Sciences of Ukraine, Kyiv (Ukraine). Inst. for Problems of Materials Science
  3. Tokyo Inst. of Technology, Yokohama (Japan)
  4. National Academy of Sciences of Ukraine, Kyiv (Ukraine). Inst. of Physics
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
OSTI Identifier:
1649334
Alternate Identifier(s):
OSTI ID: 1646708
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Applied Physics
Additional Journal Information:
Journal Volume: 128; Journal Issue: 5; Journal ID: ISSN 0021-8979
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Machine learning for materials design and discovery; Ferroelectric materials; Bayesian inference; Scanning probe microscopy; Atomic force microscopy; Dielectric properties; Spectroscopy

Citation Formats

Vasudevan, Rama K., Kelley, Kyle P., Eliseev, Eugene A., Jesse, Stephen, Funakubo, Hiroshi, Morozovska, Anna N., and Kalinin, Sergei V. Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging. United States: N. p., 2020. Web. doi:10.1063/5.0005323.
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Vasudevan, Rama K., Kelley, Kyle P., Eliseev, Eugene A., Jesse, Stephen, Funakubo, Hiroshi, Morozovska, Anna N., & Kalinin, Sergei V. Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging. United States. https://doi.org/10.1063/5.0005323
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Vasudevan, Rama K., Kelley, Kyle P., Eliseev, Eugene A., Jesse, Stephen, Funakubo, Hiroshi, Morozovska, Anna N., and Kalinin, Sergei V. Fri . "Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging". United States. https://doi.org/10.1063/5.0005323. https://www.osti.gov/servlets/purl/1649334.
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@article{osti_1649334,
title = {Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging},
author = {Vasudevan, Rama K. and Kelley, Kyle P. and Eliseev, Eugene A. and Jesse, Stephen and Funakubo, Hiroshi and Morozovska, Anna N. and Kalinin, Sergei V.},
abstractNote = {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. In this work, 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.},
doi = {10.1063/5.0005323},
journal = {Journal of Applied Physics},
number = 5,
volume = 128,
place = {United States},
year = {Fri Aug 07 00:00:00 EDT 2020},
month = {Fri Aug 07 00:00:00 EDT 2020}
}
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