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Phase diagrams classification based on machine learning and phenomenological investigation of physical properties in K1 − xNaxNbO3 thin films.
- Source :
- Journal of Applied Physics; 4/21/2020, Vol. 127 Issue 15, p1-14, 14p, 2 Charts, 9 Graphs
- Publication Year :
- 2020
-
Abstract
- In this work, we have predicted and classified the temperature-misfit strain phase diagrams of (001)-oriented K<subscript>1</subscript><subscript>−</subscript><subscript>x</subscript>Na<subscript>x</subscript>NbO<subscript>3</subscript> (KNN, 0 ≤ x ≤ 0.5) thin films using three classical machine learning algorithms: k-nearest neighbors, support vector machine, and deep neural networks, which have a very excellent prediction accuracy rate of about 99%. Furthermore, various physical properties including ferroelectric, dielectric, piezoelectric, and electrocaloric properties have been calculated and studied based on the phenomenological Landau–Devonshire theory. The calculated results show that the dielectric constant ɛ<subscript>33</subscript>, piezoelectric coefficient d<subscript>33</subscript>, and isothermal entropy change ΔS of the KNN thin films can be enhanced at the orthorhombic–rhombohedral phase boundary. This work will provide theoretical guidance for experimental studies of KNN thin films. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00218979
- Volume :
- 127
- Issue :
- 15
- Database :
- Complementary Index
- Journal :
- Journal of Applied Physics
- Publication Type :
- Academic Journal
- Accession number :
- 142830344
- Full Text :
- https://doi.org/10.1063/5.0004167