Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Rama K. Vasudevan; Kyle P. Kelley; Jacob Hinkle; Hiroshi Funakubo; Stephen Jesse; Sergei V. Kalinin; Maxim Ziatdinov

doi:10.1021/acsnano.0c10239

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Rama K. Vasudevan, Kyle P. Kelley, Jacob Hinkle, Hiroshi Funakubo, Stephen Jesse, Sergei V. Kalinin, Maxim Ziatdinov

Research output: Contribution to journal › Article › peer-review

25 Scopus citations

Abstract

Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.

Original language	English
Pages (from-to)	11253-11262
Number of pages	10
Journal	ACS Nano
Volume	15
Issue number	7
DOIs	https://doi.org/10.1021/acsnano.0c10239
State	Published - Jul 27 2021

Funding

This research ( GPim development, AE experimentation) was conducted at the Center for Nanophase Materials Sciences, which also provided support (R.K.V., M.Z., S.J., S.V.K.) and is a US DOE Office of Science User Facility. Simulated data analysis (K.P.K.) was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division. This work (sample preparation) was partially supported by JSPSKAKENHI Grant Nos. 15H04121 and 26220907 (H.F.).

Keywords

Bayesian optimization
active learning
automated experiments
electromechanics
ferroelectrics
piezoresponse force microscopy

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10.1021/acsnano.0c10239

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title = "Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics",

abstract = "Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.",

keywords = "Bayesian optimization, active learning, automated experiments, electromechanics, ferroelectrics, piezoresponse force microscopy",

author = "Vasudevan, {Rama K.} and Kelley, {Kyle P.} and Jacob Hinkle and Hiroshi Funakubo and Stephen Jesse and Kalinin, {Sergei V.} and Maxim Ziatdinov",

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AU - Kelley, Kyle P.

AU - Hinkle, Jacob

AU - Funakubo, Hiroshi

AU - Jesse, Stephen

AU - Kalinin, Sergei V.

AU - Ziatdinov, Maxim

PY - 2021/7/27

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AB - Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.

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KW - active learning

KW - automated experiments

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KW - ferroelectrics

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Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

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Keywords

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