Prediction of the lattice constants of pyrochlore compounds using machine learning

The process of material discovery and design can be simplified and accelerated if we can effectively learn from existing data. In this study, we explore the use of machine learning techniques to learn the relationship between the structural properties of pyrochlore compounds and their lattice constants. We proposed a support vector regression (SVR) and artificial neural network (ANN) models to predict the lattice constants of pyrochlore materials. Our study revealed that the lattice constants of pyrochlore compounds, generically represented A(2)B(2)O(7) (A and B cations), can be effectively predicted from the ionic radii and electronegativity data of the constituting elements. Furthermore, we compared the accuracy of our ANN, SVR models with an existing linear model in the literature. The analysis revealed that the SVR model exhibits a better accuracy with a correlation coefficient of 99.34 percent with the experimental data. Therefore, the proposed SVR model provides an avenue toward a precise estimation of the lattice constants of pyrochlore compounds.