Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors
Abstract
Machine learning of dynamic responses allows determination of structural phase transitions in relaxor ferroelectrics.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 02, 2018
- Source ID
- 10.1126/sciadv.aap8672
Entities
People
- Dawei Zhang
- Linglong Li
- Rama K. Vasudevan
- Sergei V. Kalinin
- Stephen Jesse
- Yaodong Yang
- Zuo-Guang Ye
Organizations
- China Scholarship Council
- National Natural Science Foundation of China
- Natural Sciences and Engineering Research Council
- Oak Ridge National Laboratory
- Office of Naval Research
- Simon Fraser University
- United States Department of Energy
- University of New South Wales
- Xi'an Jiaotong University