Publication:
Atomic Structure Simulation and Properties? Prediction using Machine Learning on Neodymium Oxide Nanoparticles Zinc Tellurite Glasses Aided by FTIR and TEM Analysis

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dc.citedby0
dc.contributor.authorNazrin S.N.en_US
dc.contributor.authorZaman H.B.en_US
dc.contributor.authorJothi N.en_US
dc.contributor.authorJouay D.en_US
dc.contributor.authorLahrach B.en_US
dc.contributor.authorHalimah M.K.en_US
dc.contributor.authorid57201365934en_US
dc.contributor.authorid57226220128en_US
dc.contributor.authorid54928769700en_US
dc.contributor.authorid59377286400en_US
dc.contributor.authorid59377286500en_US
dc.contributor.authorid12784680800en_US
dc.date.accessioned2025-03-03T07:46:10Z
dc.date.available2025-03-03T07:46:10Z
dc.date.issued2024
dc.description.abstractThe 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. ? 2024, Politeknik Negeri Padang. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.62527/joiv.8.3.3097
dc.identifier.epage1486
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85207006029
dc.identifier.spage1476
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85207006029&doi=10.62527%2fjoiv.8.3.3097&partnerID=40&md5=36d0d31a51e0d3360bf384e30df29c8a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36965
dc.identifier.volume8
dc.pagecount10
dc.publisherPoliteknik Negeri Padangen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleInternational Journal on Informatics Visualization
dc.titleAtomic Structure Simulation and Properties? Prediction using Machine Learning on Neodymium Oxide Nanoparticles Zinc Tellurite Glasses Aided by FTIR and TEM Analysisen_US
dc.typeArticleen_US
dspace.entity.typePublication
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