Atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by FTIR and TEM analysis
The optical, structural, and physical characteristics of zinc tellurite glasses doped with neodymium oxide nanoparticles, which are produced by the melt-quenching method, were examined in this work. The amorphous character of the glasses was verified by XRD analysis. Using the Pair Distribution Func...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/116682/ http://psasir.upm.edu.my/id/eprint/116682/1/116682.pdf |
| _version_ | 1848867068132196352 |
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| author | Nazrin, S.N. Zaman, Halimah Badioze Jothi, Neesha Jouay, Doha Lahrach, Badreddine Halimah, M.K. |
| author_facet | Nazrin, S.N. Zaman, Halimah Badioze Jothi, Neesha Jouay, Doha Lahrach, Badreddine Halimah, M.K. |
| author_sort | Nazrin, S.N. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The optical, structural, and physical characteristics of zinc tellurite glasses doped with neodymium oxide nanoparticles, which are produced by the melt-quenching method, were examined in this work. The amorphous character of the glasses was verified by XRD analysis. Using the Pair Distribution Function (PDF) and Monte Carlo simulations and visualisation for precise molecule distribution representation, an intuitive Python interface was created to emphasize these features. The density increased with increasing Nd2O3 concentrations, from 5346 to 5606 kg/cm2. Density data was used to infer the molar volume. The best projected density was achieved by the Gradient Boosting Regressor model, with a R2 of 0.9988 and an RMSE of 0.0032; the best predicted molar volume was achieved by linear regression, with a R2 of 1 and an RMSE of 2.67e-15. These models successfully represent the correlations between dopant concentration and glass properties, advancing our knowledge of the optical properties for further glass technology research. |
| first_indexed | 2025-11-15T14:30:37Z |
| format | Article |
| id | upm-116682 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:30:37Z |
| publishDate | 2024 |
| publisher | Politeknik Negeri Padang |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1166822025-04-15T00:05:33Z http://psasir.upm.edu.my/id/eprint/116682/ Atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by FTIR and TEM analysis Nazrin, S.N. Zaman, Halimah Badioze Jothi, Neesha Jouay, Doha Lahrach, Badreddine Halimah, M.K. The optical, structural, and physical characteristics of zinc tellurite glasses doped with neodymium oxide nanoparticles, which are produced by the melt-quenching method, were examined in this work. The amorphous character of the glasses was verified by XRD analysis. Using the Pair Distribution Function (PDF) and Monte Carlo simulations and visualisation for precise molecule distribution representation, an intuitive Python interface was created to emphasize these features. The density increased with increasing Nd2O3 concentrations, from 5346 to 5606 kg/cm2. Density data was used to infer the molar volume. The best projected density was achieved by the Gradient Boosting Regressor model, with a R2 of 0.9988 and an RMSE of 0.0032; the best predicted molar volume was achieved by linear regression, with a R2 of 1 and an RMSE of 2.67e-15. These models successfully represent the correlations between dopant concentration and glass properties, advancing our knowledge of the optical properties for further glass technology research. Politeknik Negeri Padang 2024-09-30 Article PeerReviewed text en cc_by_nc_sa_4 http://psasir.upm.edu.my/id/eprint/116682/1/116682.pdf Nazrin, S.N. and Zaman, Halimah Badioze and Jothi, Neesha and Jouay, Doha and Lahrach, Badreddine and Halimah, M.K. (2024) Atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by FTIR and TEM analysis. International Journal on Informatics Visualization, 8 (3). pp. 1476-1486. ISSN 2549-9904; eISSN: 2549-9904 https://joiv.org/index.php/joiv/article/view/3097 10.62527/joiv.8.3.3097 |
| spellingShingle | Nazrin, S.N. Zaman, Halimah Badioze Jothi, Neesha Jouay, Doha Lahrach, Badreddine Halimah, M.K. Atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by FTIR and TEM analysis |
| title | Atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by FTIR and TEM analysis |
| title_full | Atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by FTIR and TEM analysis |
| title_fullStr | Atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by FTIR and TEM analysis |
| title_full_unstemmed | Atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by FTIR and TEM analysis |
| title_short | Atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by FTIR and TEM analysis |
| title_sort | atomic structure simulation and properties’ prediction using machine learning on neodymium oxide nanoparticles zinc tellurite glasses aided by ftir and tem analysis |
| url | http://psasir.upm.edu.my/id/eprint/116682/ http://psasir.upm.edu.my/id/eprint/116682/ http://psasir.upm.edu.my/id/eprint/116682/ http://psasir.upm.edu.my/id/eprint/116682/1/116682.pdf |