Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors-Reference-Cited by-同舟云学术

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

Published:2018-03-02 Issue:3 Volume:4 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Li Linglong¹²³,Yang Yaodong³^ORCID,Zhang Dawei⁴,Ye Zuo-Guang⁵,Jesse Stephen¹²,Kalinin Sergei V.¹²^ORCID,Vasudevan Rama K.¹²^ORCID

Affiliation:

1. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

2. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

3. Frontier Institute of Science and Technology, State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China.

4. School of Materials Science and Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia.

5. Department of Chemistry and 4D LABS, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada.

Abstract

Machine learning of dynamic responses allows determination of structural phase transitions in relaxor ferroelectrics.

Funder

Office of Naval Research

U.S. Department of Energy

Natural Sciences and Engineering Research Council of Canada

Chinese Scholarship Council

MOE innovation team

National Natural Science Foundation of China

Publisher