Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors
Published 2018 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors
Authors
Keywords
-
Journal
Science Advances
Volume 4, Issue 3, Pages eaap8672
Publisher
American Association for the Advancement of Science (AAAS)
Online
2018-04-17
DOI
10.1126/sciadv.aap8672
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Detection of Phase Transition via Convolutional Neural Networks
- (2017) Akinori Tanaka et al. JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN
- Slush-like polar structures in single-crystal relaxors
- (2017) Hiroyuki Takenaka et al. NATURE
- Machine learning: New tool in the box
- (2017) Lenka Zdeborová Nature Physics
- Experimental quantum Hamiltonian learning
- (2017) Jianwei Wang et al. Nature Physics
- Learning phase transitions by confusion
- (2017) Evert P. L. van Nieuwenburg et al. Nature Physics
- A structural approach to relaxation in glassy liquids
- (2016) S. S. Schoenholz et al. Nature Physics
- Subterahertz dielectric relaxation in lead-free Ba(Zr,Ti)O3 relaxor ferroelectrics
- (2016) D. Wang et al. Nature Communications
- Assembly and phase transitions of colloidal crystals
- (2016) Bo Li et al. Nature Reviews Materials
- Acoustic Detection of Phase Transitions at the Nanoscale
- (2015) Rama K. Vasudevan et al. ADVANCED FUNCTIONAL MATERIALS
- Mechanical Switching of Nanoscale Multiferroic Phase Boundaries
- (2015) Yong-Jun Li et al. ADVANCED FUNCTIONAL MATERIALS
- The Higgs mode in disordered superconductors close to a quantum phase transition
- (2015) Daniel Sherman et al. Nature Physics
- Finite-temperature properties of the relaxorPbMg1/3Nb2/3O3from atomistic simulations
- (2015) A. Al-Barakaty et al. PHYSICAL REVIEW B
- Giant elastic tunability in strained BiFeO3 near an electrically induced phase transition
- (2015) Q Li et al. Nature Communications
- Nanoscale Mechanical Softening of Morphotropic BiFeO3
- (2014) Yooun Heo et al. ADVANCED MATERIALS
- Band Excitation in Scanning Probe Microscopy: Recognition and Functional Imaging
- (2014) S. Jesse et al. Annual Review of Physical Chemistry
- On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets
- (2014) Aaron Gilad Kusne et al. Scientific Reports
- Parameter Space Compression Underlies Emergent Theories and Predictive Models
- (2013) B. B. Machta et al. SCIENCE
- Spectroscopic imaging in piezoresponse force microscopy: New opportunities for studying polarization dynamics in ferroelectrics and multiferroics
- (2012) R.K. Vasudevan et al. MRS Communications
- Thermally activated avalanches: Jamming and the progression of needle domains
- (2011) E. K. H. Salje et al. PHYSICAL REVIEW B
- Above-room-temperature ferroelectricity in a single-component molecular crystal
- (2010) Sachio Horiuchi et al. NATURE
- Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy
- (2009) Stephen Jesse et al. NANOTECHNOLOGY
- Nanotwins and phases in high-strain Pb(Mg1/3Nb2/3)1−xTixO3 crystal
- (2008) C.-S. Tu et al. JOURNAL OF APPLIED PHYSICS
- Origin of morphotropic phase boundaries in ferroelectrics
- (2008) Muhtar Ahart et al. NATURE
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search