Classification of perovskites with supervised self-organizing maps

In this work supervised self-organizing maps were used for structural classification of perovskites. For this purpose, structural data for total number of 286 perovskites, belonging to ABO3 and/or A2BB'O6 types, were collected from literature: 130 of these are cubic, 85 orthorhombic and 71 monoclinic. For classification purposes, the effective ionic radii of the cations, electronegativities of the cations in B-position, as well as, the oxidation states of these cations, were used as input variables. The parameters of the developed models, as well as, the most suitable variables for classification purposes were selected using genetic algorithms. Two-third of all the compounds were used in the training phase. During the optimization process the performances of the models were checked using cross-validation leave-1/10-out. The performances of obtained solutions were checked using the test set composed of the remaining one-third of the compounds. The obtained models for classification of these three classes of perovskite compounds show very good results. Namely, the classification of the compounds in the test set resulted in small number of discrepancies (4.2-6.4%) between the actual crystallographic class and the one predicted by the models. All these results are strong arguments for the validity of supervised self-organizing maps for performing such types of classification. Therefore, the proposed procedure could be successfully used for crystallographic classification of perovskites in one of these three classes.     

Prediction of the unit cell edge length of cubic A 2 2+ 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 A ₂ ²⁺ 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 A ₂ ²⁺ 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.     

Solution combustion synthesis of YCoO3 and investigation of its catalytic properties by cyclic voltammetery

In this paper, the synthesis of YCoO₃ by solution combustion method and investigation of its catalytic activity using cyclic voltammetry is presented. The perovskite phase was obtained by thermal initializing of the solutions of the metal nitrates and the fuel (urea). The obtained solid precursor was further heated yielding the perovskite phase. The obtained perovskite compound has orthorhombic unit cell, within the space group Pnma, with unit cell parameters a = 5.4223 ?, b = 7.3657 ?, and c = 5.1385 ?. The catalytic activity of the prepared perovskite was investigated by cyclic voltammetry using YCoO₃-modified paraffin impregnated graphite electrode, in several electrolytes. It was found that the YCoO₃ perovskite has a distinct catalytic activity towards the oxidation of chloride anions in which Co³⁺ ions being the active centers. Also, this material enhances the oxidation of methanol in KOH.     

Examination of the influence of different variables on prediction of unit cell parameters in perovskites using counter‐propagation artificial neural networks

In this work, the unit cell parameter (a) of the series of cubic ABX₃ perovskites was modeled using counter‐propagation artificial neural networks, and the influence of different input variables was examined by using algorithm for automatic adjustment of the relative importance of the variables. The input variables used in this model were the ionic radii of A, B, and X as well as the oxidation state (z) and the electronegativity (χ) of the anion. The developed models have good generalization performances—good agreement between experimental and predicted values for lattice parameter. One of the important outcomes from this work is obtained from the results of the automatic adjustment of the relative importance of input variables. That is to say, this analysis gave us an insight that the most pronounced influence on the successful prediction of the unit cell parameter of the analyzed data set of cubic ABX₃ perovskites has the effective ionic radii of B‐cation. In addition to this, it may be concluded that the separation of the compounds in different regions of counter‐propagation artificial neural networks was predominantly influenced by the input variables with regard to the physical parameters of the anion. Copyright © 2012 John Wiley & Sons, Ltd.