High-Throughput Screening of Perovskite Alloys for Piezoelectric Performance and Formability

Abstract

We screen a large chemical space of perovskite alloys for systems with the right properties to accommodate a morphotropic phase boundary (MPB) in their composition-temperature phase diagram, a crucial feature for high piezoelectric performance. We start from alloy end-points previously identified in a high-throughput computational search. An interpolation scheme is used to estimate the relative energies between different perovskite distortions for alloy compositions with a minimum of computational effort. Suggested alloys are further screened for thermodynamic stability. The screening identifies alloy systems already known to host a MPB, and suggests a few new ones that may be promising candidates for future experiments. Our method of investigation may be extended to other perovskite systems, e.g., (oxy-)nitrides, and provides a useful methodology for any application of high-throughput screening of isovalent alloy systems.

References in corpus (2)

• AFLOW: An automatic framework for high-throughput materials discovery
• Frustration of tilts and A-site driven ferroelectricity in KNbO_3-LiNbO_3 alloys

Cited by in corpus (8)

• Predicting the thermodynamic stability of perovskite oxides using machine learning models
• High throughput thermal conductivity of high temperature solid phases: The case of oxide and fluoride perovskites
• Efficiency of Generalized Regular k-point Grids
• Large Piezoelectric Response of van der Waals Layered Solids
• Microscopic picture of paraelectric perovskites from structural prototypes
• Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design
• Predicting the Volumes of Crystals
• Machine learning, phase stability, and disorder with the Automatic Flow Framework for Materials Discovery