Lattice constant prediction of cubic and monoclinic perovskites using neural networks and support vector regression

In the study of crystalline materials, the lattice constant (LC) of perovskites compounds play important role in the identification of materials. It reveals various interesting properties. In this study, we have employed Support Vector Regression, Artificial Neural Network, and Generalized Regression Neural Network based Computational Intelligent (CI) techniques to predict LC of cubic and monoclinic perovskites. Due to their interesting physiochemical properties, investigations in modeling the structural properties of perovskites have gained considerable attention. A dataset of a reasonable number of cubic and monoclinic perovskites are collected from the current literature. The Cl techniques can efficiently correlate the LC of the perovskites materials with the ionic radii of constituent elements. A performance analysis of Cl techniques is carried out with Multiple Linear Regression techniques, SPuDS software, and Density-Functional Theory. We have observed that the CI techniques yield accurate LC prediction as against the conventional approaches. Availability: Matlab based computer program developed for this work is available on request. (C) 2010 Elsevier B.V. All rights reserved.