Predicting lattice constant of complex cubic perovskites using computational intelligence

Recently in the field of materials science, advanced computational intelligence (CI) based approaches are gaining substantial importance for modeling the quantitative structure to properties relationship. In this study, we have used support vector regression, random forest, generalized regression neural network, and multiple linear regression based CI approaches to predict lattice constants (LCs) of complex cubic perovskites. We have collected reasonable number of perovskites compounds from the recent literature of materials science. The CI models are developed using 100 training compounds and the generalized performance is estimated for the novel 97 compounds. Our analysis highlights the improved prediction performance of CI approaches than the well-known SPuDS software, which is extensively used in crytsallography. We further observed that, for some of the compounds, the larger prediction error provided by the CI models is correlated with the structure deviation of the compounds from its ideal cubic symmetry.