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

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.

Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression

“…By relying solely on the predictor variables, the resulting SVR model can accurately generalize to future unknown data [91]. SVR is based on sound mathematical theory and its optimization problem has an optimal and global solution in the form of linearly constrained quadratic programming [92]. Linear regression constructed with SVR is generally defined as contained in…”