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.
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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
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DOI: https://doi.org/10.1007/978-3-642-02457-3_7
Publisher Name: Springer, Berlin, Heidelberg
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