Phase diagrams classification based on machine learning and phenomenological investigation of physical properties in K1-x Nax NbO3 thin films

Abstract

In this work, we have predicted and classified the temperature-misfit strain phase diagrams of (001)-oriented K1-xNaxNbO3 (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 σ33, piezoelectric coefficient d33, 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.