Abstract
Researchers are taking interest in the computational prediction models to efficiently predict the structure of perovskites. we are using Support Vector Regression, Artificial Neural Network, Multiple Linear Regression and SPuDS program based approaches in predicting the lattice constants (LC) of double perovskites of A2BB’O6-type. These prediction models correlate the LC to atomic parameters i.e., size of ionic radii, electro-negativity, and oxidation state. These models are developed using training data. Their performance is then estimated for validation data. To investigate the generalization capability, 48 new perovskites are also collected from recent literature. Analysis shows that SVR based proposed models are more accurate and generalized, reducing the prediction error effectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baran, E.J.: Structural chemistry and physicochemical properties of perovskite-like materials. Catalysis Today 8, 133–151 (1990)
Wolfram, T., Ellialtioglu, S.: Electronic and Optical Properties of d-Band Perovskites. Cambridge University Press, Cambridge (2006)
Azad, A.K.: Synthesis, Structure, and Magnetic Properties of Double Perovskites of the type A2MnBO6 and A2FeBO6 (A=Ca, Sr, Ba; B= W, Mo, Cr). Ph.D. Thesis. Göte-borg University, Sweden (2004)
Bouville, M., Ahluwalia, R.: Effect of lattice-mismatch-induced strains on coupled diffu-sive and displacive phase transformations. Physical Review B - Condensed Matter and Materials Physics 75, 054110–054118 (2007)
Philipp, J.B., Majewski, P., Alff, L., Erb, A., Gross, R., Graf, T., Brandt, M.S., Simon, J., Walther, T., Mader, W., Topwal, D., Sarma, D.D.: Structural and doping effects in the half-metallic double perovskite A 2CrWO6 (A = Sr, Ba, and Ca). Physical Review B - Condensed Matter and Materials Physics 68, 1444311–14443113 (2003)
Serrate, D., De Teresa, J.M., Ibarra, M.R.: Double perovskites with ferromagnetism above room temperature. Journal of Physics Condensed Matter 19, 023201–023287 (2007)
Faik, A., Gateshki, M., Igartua, J.M., Pizarro, J.L., Insausti, M., Kaindl, R., Grzechnik, A.: Crystal structures and cation ordering of Sr2AlSbO6 and Sr2CoSbO6. Journal of Solid State Chemistry 181, 1759–1766 (2008)
Bokov, A.A., Protsenko, N.P., Ye, Z.G.: Relationship between ionicity, ionic radii and or-der/disorder in complex perovskites. Journal of Physics and Chemistry of Solids 61, 1519–1527 (2000)
Dimitrovska, S., Aleksovska, S., Kuzmanovski, I.: Prediction of the unit cell edge length of cubic A2BB 2006 perovskites by multiple linear regression and artificial neural networks. Central European Journal of Chemistry 3, 198–215 (2005)
Lufaso, M.W., Woodward, P.M.: Prediction of the crystal structures of perovskites using the software program SPuDS. Acta Crystallographica Section B: Structural Science 57, 725–738 (2001)
Shannon, R.D.: Revised effective ionic radii and Systematic Studies of Interatomic Dis-tances in halides and chalcogenides. Acta Cryst. A 32, 751–767 (1976)
Environmental, Chemistry & Hazardous Materials Resources, http://environmentalchemistry.com/yogi/periodic/
Xu, L., Wencong, L., Shengli, J., Yawei, L., Nianyi, C.: Support vector regression applied to materials optimization of sialon ceramics. Chemometrics and Intelligent Laboratory Systems 82, 8–14 (2006)
Li, J., Liu, H., Yao, X., Liu, M., Hu, Z., Fan, B.: Quantitative structure-activity relationship study of acyl ureas as inhibitors of human liver glycogen phosphorylase using least squares support vector machines. Chemometrics and Intelligent Laboratory Systems 87, 139–146 (2007)
Pan, Y., Jiang, J., Wang, R., Cao, H., Cui, Y.: Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine. Chemometr. Intell. Lab. Syst. 92, 169–178 (2008)
Smola, A., Schoelkopf, B.: A Tutorial on Support Vector Regression, vol. 14, pp. 199–222. Springer, Netherlands (2004)
Gunn, S.: Support vector machines for classification and regression. ISIS Technical Report (1999)
Majid, A., Khan, A., Mirza, A.M.: Combination of Support Vector Machines Using Ge-netic Programming. International Journal of Hybrid Intelligent System 3, 1–17 (2006)
MATLAB7.0: Mathworks, http://www.mathworks.com
Javed, S.G., Khan, A., Majid, A., Mirza, A.M., Bashir, J.: Lattice constant prediction of or-thorhombic ABO3 perovskites using support vector machines. Computational Materials Science 39, 627–634 (2007)
Woodward, P.M., Goldberger, J., Stoltzfus, M.W., Eng, H.W., Ricciardo, R.A., Santhosh, P.N., Karen, P., Moodenbaugh, A.R.: Electronic, magnetic, and structural properties of Sr2MnRuO 6 and LaSrMnRuO6 double perovskites. Journal of the American Ceramic Society 91, 1796–1806 (2008)
Kato, H., Okuda, T., Okimoto, Y., Tomioka, Y., Oikawa, K., Kamiyama, T., Tokura, Y.: Structural and electronic properties of the ordered double perovskites A2MReO6 (A = Sr,Ca; M = Mg,Sc,Cr,Mn,Fe,Co,Ni,Zn). Physical Review B - Condensed Matter and Materials Physics 69, 184412–184420 (2004)
Popov, G., Greenblatt, M., Croft, M.: Large effects of A-site average cation size on the properties of the double perovskites Ba2-xSrxMnReO6: A d5-d1 system. Physical Review B - Condensed Matter and Materials Physics 67, 244061–244069 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Majid, A., Farooq Ahmad, M., Choi, TS. (2009). Lattice Constant Prediction of A2BB’O6 Type Double Perovskites. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2009. ICCSA 2009. Lecture Notes in Computer Science, vol 5593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02457-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-02457-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02456-6
Online ISBN: 978-3-642-02457-3
eBook Packages: Computer ScienceComputer Science (R0)