Automating the method for correlation selection of perovskites

N. Mykytenko, A. Kiv, D. Fuks

Abstract


In this paper it is proposed a new approach which allows automating the selection of perovskites ABO3, which do not exhibit the required properties. First of all we use the procedure for determining the correlation «the descriptor - composition of the perovskite compound» and investigate compounds with high ionic conductivity (σ). The features of known descriptors that allow optimizing the parameters of perovskites ABO3 are also discussed. While calculating the correlations we used a descriptor that consists of the ratio of ionic radii RA/RB and potentials of ionization of valence electrons VA/VB of A and B cations. Special attention is given to determination of correlation between the descriptor and σ by using a special computer program, which has been developed for the selection of perovskites that provide the large correlation coefficients and hence high ionic conductivity.
The computer program automatically performs correlation selection procedure for perovskites by consistent realizing the following steps:
1. The linear approximation of the full set of points based on the regression analysis.
2. Calculation of the correlation coefficient. 3. To search for "worst" point from the full set of points by sorting them and removing the "worst" point. 3.1. For each remote point repeated linear approximation of points is performed and the correlation coefficient for a set of remaining points is calculated. 3.2. Based on the found set of correlation coefficients the "worst" point is determined. 4. The "worst" points are sequentially removed as long as the correlation coefficient reaches the desired magnitude.
More over there have been analyzed the features of remote compounds that distinguish them from the main array.


Keywords


perovskites; ionic conductivity; descriptor; correlation; regression analysis; computer modeling

References


1. Muller O. Crystal chemistry of non-metallic materials. Vol. 4. The major ternary structural families / O. Muller, R. Roy // Acta Cryst. B. – 1975. – Vol. 31. – P. 2944.
2. Shannon R. D. Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides / R. D. Shannon // Acta Cryst. A. – 1976. – Vol. 32. – P. 751-767.
3. Pauling L. The sizes of ions and the structure of ionic crystals / L. Pauling // J. Am. Chem. Soc. – 1927. – Vol. 49. – P. 765-790.
4. Kumar A. Prediction of formability in perovskite-type oxides / A. Kumar, A.S. Verma, S.R. Bhardwaj // Open Applied Physics Journal. – 2008. – Vol. 1. – P. 11-19.
5. Reaney I.M. Dielectric and structural characteristics of perovskites and related materials as a function of tolerance factor / I.M. Reaney, R. Ubic // Ferroelectrics. – 1999. – Vol. 228, №1. – P. 23-38.
6. Fuks D.L. Correlation between the composition and the rate of ionic transport in perovskites / D. L. Fuks, A. E. Kiv // Adv. Mat. Lett. – 2013. –Vol. 4, №5. – P. 328-331.
7. Mykytenko N. Correlation Selection of Perovskites with Optimal Parameters / N. Mykytenko, A. Kiv, D. Fuks // Adv. Mat. Lett. – 2016. – Vol. 7, №4. – P. 10–14.
8. Kendall K.R. Recent developments in perovskite-based oxide ion conductors / K.R. Kendall, C. Navas, J.K. Thomas, H. Loye // Solid State Ionics. – 1995. – Vol. 82, №3. – P. 215-223.
9. Sinha A. Study on ionic and electronic transport properties of calcium-doped GdAlO3/ A. Sinha, H. Näfe, B.P. Sharma, P. Gopalan // J. Electrochem. Soc. – 2008. – Vol. 155, №3. – P. 309-314.
10. Mitchell R. H. Perovskites: Modern and Ancient / R. H. Mitchell. – Thunder Bay, Ontario: Almaz Press, 2002. – 318 p.
11. Ishihara T. Doped LaGaO3 perovskite-type oxide as a new oxide ionic conductor / T. Ishihara, H. Matsuda, Y. Takita // J. Am. Chem. Soc. – 1994. – Vol. 116, №9. – P.3801- 3803.
12. Draper N.R. Applied regression analysis, 3rd edition / N.R. Draper, H. Smith. – New York: John Wiley & Sons Inc., 1998. – 736 p.


Full Text: PDF (Українська)
Archive
2013 16
2014 16
2015 16
2016 1
2017 1
2018 1
2019

1

User

Language

Journal Content

Browse