Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2Ti0.8O3 Thin Films

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.

doi:10.1002/adma.201800701

Title: Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr_0.2Ti_0.8O₃ Thin Films

Journal Article · 28 May 2018 · Advanced Materials

DOI: https://doi.org/10.1002/adma.201800701 · OSTI ID:1439928

^[1]; Cao, Ye ^[2]; Naul, Brett ^[3]; Pandya, Shishir ^[1]; van der Walt, Stefan ^[4]; Luo, Aileen I. ^[1]; Maher, Joshua T. ^[1];

^[5];

^[6]; Martin, Lane W. ^[7]

Univ. of California, Berkeley, CA (United States). Dept. of Materials Science & Engineering
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
Univ. of California, Berkeley, CA (United States). Dept. of Astronomy
Univ. of California, Berkeley, CA (United States). Berkeley Inst. of Data Science
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
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)
Univ. of California, Berkeley, CA (United States). Dept. of Materials Science & Engineering; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); US Army Research Office (ARO); National Science Foundation (NSF); Gordon and Betty Moore Foundation; USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division

Grant/Contract Number:: AC05-00OR22725; SC0012375; W911NF-14-1-0104; AC02-05CH11231; DMR-1708615; OISE-1545907; DMR-1451219; 1251274

OSTI ID:: 1439928

Alternate ID(s):: OSTI ID: 1439355; OSTI ID: 1506335

Journal Information:: Advanced Materials, Vol. 30, Issue 28; ISSN 0935-9648

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 22 works

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