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 consta...
| Main Authors: | , , , |
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| Format: | Article |
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Springer
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/102837/ |
| _version_ | 1848863884576817152 |
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| author | Alade, Ibrahim Olanrewaju Oyedeji, Mojeed Opeyemi Abd Rahman, Mohd Amiruddin Saleh, Tawfik A. |
| author_facet | Alade, Ibrahim Olanrewaju Oyedeji, Mojeed Opeyemi Abd Rahman, Mohd Amiruddin Saleh, Tawfik A. |
| author_sort | Alade, Ibrahim Olanrewaju |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T13:40:01Z |
| format | Article |
| id | upm-102837 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:40:01Z |
| publishDate | 2022 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1028372024-06-30T02:28:56Z http://psasir.upm.edu.my/id/eprint/102837/ Prediction of the lattice constants of pyrochlore compounds using machine learning Alade, Ibrahim Olanrewaju Oyedeji, Mojeed Opeyemi Abd Rahman, Mohd Amiruddin Saleh, Tawfik A. 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. Springer 2022 Article PeerReviewed Alade, Ibrahim Olanrewaju and Oyedeji, Mojeed Opeyemi and Abd Rahman, Mohd Amiruddin and Saleh, Tawfik A. (2022) Prediction of the lattice constants of pyrochlore compounds using machine learning. Soft Computing, 26 (17). pp. 8307-8315. ISSN 1432-7643; ESSN: 1433-7479 https://link.springer.com/article/10.1007/s00500-022-07218-1 10.1007/s00500-022-07218-1 |
| spellingShingle | Alade, Ibrahim Olanrewaju Oyedeji, Mojeed Opeyemi Abd Rahman, Mohd Amiruddin Saleh, Tawfik A. Prediction of the lattice constants of pyrochlore compounds using machine learning |
| title | Prediction of the lattice constants of pyrochlore compounds using machine learning |
| title_full | Prediction of the lattice constants of pyrochlore compounds using machine learning |
| title_fullStr | Prediction of the lattice constants of pyrochlore compounds using machine learning |
| title_full_unstemmed | Prediction of the lattice constants of pyrochlore compounds using machine learning |
| title_short | Prediction of the lattice constants of pyrochlore compounds using machine learning |
| title_sort | prediction of the lattice constants of pyrochlore compounds using machine learning |
| url | http://psasir.upm.edu.my/id/eprint/102837/ http://psasir.upm.edu.my/id/eprint/102837/ http://psasir.upm.edu.my/id/eprint/102837/ |