Prediction of the unit cell edge length of cubic A22+BB′O6 perovskites by multiple linear regression and artificial neural networks

The unit cell edge length, a, of a set of complex cubic perovskites having the general formula A22+BB′O6 is predicted using two methodologies: multiple linear regression and artificial neural neworks. The unit cell edge length is expressed as a function of six independent variables: the effective ionic radii of the constituents (A, B and B′), the electronegativities of B and B′, and the oxidation state of B. In this analysis, 147 perovskites of the A22+BB′O6 type, having the cubic structure and belonging to the Fm3m space group, are included. They are divided in two sets; 98 compounds are used in the calibration set and 49 are used in the test set. Both models give consistent results and could be successfully use to predict the lattice cell parameter of new members of this series.