Perovskite tetragonality modeling for functional properties enhancement using Newtonian search based support vector regression computational method

Peter Chibuike Okoye; Samuel Ogochukwu Azi; Taoreed O. Owolabi

doi:10.46481/jnsps.2022.248

Authors

Peter Chibuike Okoye
Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, 342111, Ondo State
Samuel Ogochukwu Azi
Department of Physics, University of Benin, Benin City, Edo State, Nigeria.
Taoreed O. Owolabi
[email protected]

Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, 342111, Ondo State, Nigeria

Keywords:

Support vector regression; tetragonality; distortion; perovskite; Newtonian based gravitational search algorithm.

Abstract

Tetragonality occurs as a result of stretching the crystal structural lattice of perovskite along one of its lattice vectors such that the three axes are mutually perpendicular with two of the axes having equal lengths. This tetragonality distortion easily triggers functional properties such as pyroelectricity, ferroelectricity, capacitance and piezoelectricity among others, while synthesizing functional ceramics for a particular application. This work addresses and circumvents the challenges of experimental stress involved in functional ceramics synthesis by developing Newtonian search based support vector regression (GSB-SVR) model for perovskite tetragonality prediction using dopants concentration and ionic radii as the model predictors. The performance of proposed GSB-SVR model is compared with the existing method and better performance is obtained. The influence of lanthanides and zirconium incorporation on functional ceramics on the material tetragonality is also modeled by the developed GSB-SVR model. The precision of the developed model, its easily fetched predictors and pre-laboratory ability to effectively and efficiently model the perovskite tetragonality are of high importance in tailoring and enhancing functional properties of materials for desired applications.

Dimensions

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