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. 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.
journal_name
Sci Advjournal_title
Science advancesauthors
Li L,Yang Y,Zhang D,Ye ZG,Jesse S,Kalinin SV,Vasudevan RKdoi
10.1126/sciadv.aap8672subject
Has Abstractpub_date
2018-03-30 00:00:00pages
eaap8672issue
3issn
2375-2548pii
aap8672journal_volume
4pub_type
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