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Title: Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors

Abstract

Exploration of phase transitions and construction of associated phase diagrams are of fundamental importance for condensed matter physics and materials science alike, and remain the focus of extensive research for both theoretical and experimental studies. For the latter, comprehensive studies involving scattering, thermodynamics, and modeling are typically required. We present a new approach to data mining multiple realizations of collective dynamics, measured through piezoelectric relaxation studies, to identify the onset of a structural phase transition in nanometer-scale volumes, that is, the probed volume of an atomic force microscope tip. Machine learning is used to analyze the multidimensional data sets describing relaxation to voltage and thermal stimuli, producing the temperature-bias phase diagram for a relaxor crystal without the need to measure (or know) the order parameter. The suitability of the approach to determine the phase diagram is shown with simulations based on a two-dimensional Ising model. Finally, these results indicate that machine learning approaches can be used to determine phase transitions in ferroelectrics, providing a general, statistically significant, and robust approach toward determining the presence of critical regimes and phase boundaries.

Authors:
 [1]; ORCiD logo [2];  [3];  [4];  [5]; ORCiD logo [5]; ORCiD logo [5]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Xi’an Jiaotong Univ., Shaanxi (China)
  2. Xi’an Jiaotong Univ., Shaanxi (China)
  3. Univ. of New South Wales, Sydney, NSW (Australia)
  4. Simon Fraser Univ., Burnaby, BC (Canada)
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
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); Natural Science Foundation (NSF)
OSTI Identifier:
1435239
Grant/Contract Number:  
AC05-00OR22725; N00014-12-1-1045; N00014-16-1-6301; 203773; 51431007; 51321003
Resource Type:
Accepted Manuscript
Journal Name:
Science Advances
Additional Journal Information:
Journal Volume: 4; Journal Issue: 3; Journal ID: ISSN 2375-2548
Publisher:
AAAS
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY

Citation Formats

Li, Linglong, Yang, Yaodong, Zhang, Dawei, Ye, Zuo-Guang, Jesse, Stephen, Kalinin, Sergei V., and Vasudevan, Rama K. Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors. United States: N. p., 2018. Web. doi:10.1126/sciadv.aap8672.
Li, Linglong, Yang, Yaodong, Zhang, Dawei, Ye, Zuo-Guang, Jesse, Stephen, Kalinin, Sergei V., & Vasudevan, Rama K. Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors. United States. doi:10.1126/sciadv.aap8672.
Li, Linglong, Yang, Yaodong, Zhang, Dawei, Ye, Zuo-Guang, Jesse, Stephen, Kalinin, Sergei V., and Vasudevan, Rama K. Fri . "Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors". United States. doi:10.1126/sciadv.aap8672. https://www.osti.gov/servlets/purl/1435239.
@article{osti_1435239,
title = {Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors},
author = {Li, Linglong and Yang, Yaodong and Zhang, Dawei and Ye, Zuo-Guang and Jesse, Stephen and Kalinin, Sergei V. and Vasudevan, Rama K.},
abstractNote = {Exploration of phase transitions and construction of associated phase diagrams are of fundamental importance for condensed matter physics and materials science alike, and remain the focus of extensive research for both theoretical and experimental studies. For the latter, comprehensive studies involving scattering, thermodynamics, and modeling are typically required. We present a new approach to data mining multiple realizations of collective dynamics, measured through piezoelectric relaxation studies, to identify the onset of a structural phase transition in nanometer-scale volumes, that is, the probed volume of an atomic force microscope tip. Machine learning is used to analyze the multidimensional data sets describing relaxation to voltage and thermal stimuli, producing the temperature-bias phase diagram for a relaxor crystal without the need to measure (or know) the order parameter. The suitability of the approach to determine the phase diagram is shown with simulations based on a two-dimensional Ising model. Finally, these results indicate that machine learning approaches can be used to determine phase transitions in ferroelectrics, providing a general, statistically significant, and robust approach toward determining the presence of critical regimes and phase boundaries.},
doi = {10.1126/sciadv.aap8672},
journal = {Science Advances},
number = 3,
volume = 4,
place = {United States},
year = {2018},
month = {3}
}

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