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Title: Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr 0.2Ti 0.8O 3 Thin Films

Abstract

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Lastly, among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.

Authors:
ORCiD logo [1];  [2];  [3];  [1];  [4];  [1];  [1]; ORCiD logo [5]; ORCiD logo [5]; ORCiD logo [5]; ORCiD logo [6];  [7]
  1. Univ. of California, Berkeley, CA (United States). Dept. of Materials Science & Engineering
  2. Univ. of Texas Arlington, Arlington, TX (United States). Dept. of Materials Science and Engineering; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials
  3. Univ. of California, Berkeley, CA (United States). Dept. of Astronomy
  4. Univ. of California, Berkeley, CA (United States). Berkeley Inst. of Data Science
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials
  6. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)
  7. Univ. of California, Berkeley, CA (United States). Dept. of Materials Science & Engineering; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); US Army Research Office (ARO); National Science Foundation (NSF); Gordon and Betty Moore Foundation
OSTI Identifier:
1439928
Alternate Identifier(s):
OSTI ID: 1439355
Grant/Contract Number:
AC05-00OR22725; SC0012375; W911NF-14-1-0104; AC02-05CH11231; DMR-1708615; OISE-1545907; DMR-1451219; 1251274
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Advanced Materials
Additional Journal Information:
Journal Name: Advanced Materials; Journal ID: ISSN 0935-9648
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; domain structures; ferroelectric materials; machine learning; PZT; scanning‐probe microscopy

Citation Formats

Agar, Joshua C., Cao, Ye, Naul, Brett, Pandya, Shishir, van der Walt, Stefan, Luo, Aileen I., Maher, Joshua T., Balke, Nina, Jesse, Stephen, Kalinin, Sergei V., Vasudevan, Rama K., and Martin, Lane W.. Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2Ti0.8O3 Thin Films. United States: N. p., 2018. Web. doi:10.1002/adma.201800701.
Agar, Joshua C., Cao, Ye, Naul, Brett, Pandya, Shishir, van der Walt, Stefan, Luo, Aileen I., Maher, Joshua T., Balke, Nina, Jesse, Stephen, Kalinin, Sergei V., Vasudevan, Rama K., & Martin, Lane W.. Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2Ti0.8O3 Thin Films. United States. doi:10.1002/adma.201800701.
Agar, Joshua C., Cao, Ye, Naul, Brett, Pandya, Shishir, van der Walt, Stefan, Luo, Aileen I., Maher, Joshua T., Balke, Nina, Jesse, Stephen, Kalinin, Sergei V., Vasudevan, Rama K., and Martin, Lane W.. Mon . "Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2Ti0.8O3 Thin Films". United States. doi:10.1002/adma.201800701.
@article{osti_1439928,
title = {Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2Ti0.8O3 Thin Films},
author = {Agar, Joshua C. and Cao, Ye and Naul, Brett and Pandya, Shishir and van der Walt, Stefan and Luo, Aileen I. and Maher, Joshua T. and Balke, Nina and Jesse, Stephen and Kalinin, Sergei V. and Vasudevan, Rama K. and Martin, Lane W.},
abstractNote = {Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Lastly, among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.},
doi = {10.1002/adma.201800701},
journal = {Advanced Materials},
number = ,
volume = ,
place = {United States},
year = {Mon May 28 00:00:00 EDT 2018},
month = {Mon May 28 00:00:00 EDT 2018}
}

Journal Article:
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