Predicting polarization enhancement in multicomponent ferroelectric superlattices

TL;DR

This paper develops a predictive model for polarization in multicomponent ferroelectric superlattices using ab initio calculations and genetic algorithms, aiming to optimize layer arrangements for enhanced polarization and minimal lattice mismatch.

Contribution

It introduces a novel combined modeling and optimization approach for designing ferroelectric superlattices with improved polarization properties.

Findings

Identified superlattice configurations with maximum polarization.

Demonstrated low lattice mismatch in optimized structures.

Provided a versatile modeling framework for layered perovskite oxides.

Abstract

Ab initio calculations are utilized as an input to develop a simple model of polarization in epitaxial short-period CaTiO3/SrTiO3/BaTiO3 superlattices grown on a SrTiO3 substrate. The model is then combined with a genetic algorithm technique to optimize the arrangement of individual CaTiO3, SrTiO3 and BaTiO3 layers in a superlattice, predicting structures with the highest possible polarization and a low in-plane lattice constant mismatch with the substrate. This modelling procedure can be applied to a wide range of layered perovskite-oxide nanostructures providing guidance for experimental development of nanoelectromechanical devices with substantially improved polar properties.