Prediction of the lattice constants of pyrochlore compounds using machine learning

被引:0
|
作者
Ibrahim Olanrewaju Alade
Mojeed Opeyemi Oyedeji
Mohd Amiruddin Abd Rahman
Tawfik A. Saleh
机构
[1] King Fahd University of Petroleum and Minerals,
[2] Universiti Putra Malaysia,undefined
来源
Soft Computing | 2022年 / 26卷
关键词
Artificial neural network; Support vector regression; Nanoparticles; Modelling; Lattice;
D O I
暂无
中图分类号
学科分类号
摘要
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 A2B2O7 (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.
引用
收藏
页码:8307 / 8315
页数:8
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