Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications
Recent advancements in glass science have focused on optimizing optical and structural properties through the incorporation of rare oxide nanoparticles, such as lanthanum and gold oxide, to enhance transparency, refractive index, and band gap energy for photonic applications. Additionally, the integ...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44999/ http://umpir.ump.edu.my/id/eprint/44999/1/Machine%20learning-driven%20prediction%20of%20optical%20and%20physical%20properties.pdf |
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| author | S.N., Nazrin Doroody, Camellia Liyana Adilla, Burhanuddin Jothi, Neesha Noraini, Ibrahim Halimah, Badioze Zaman M.H.M., Tahir Soudagar, Manzoore Elahi M. Ramesh, S. Shelare, Sagar Sharma, Shubham |
| author_facet | S.N., Nazrin Doroody, Camellia Liyana Adilla, Burhanuddin Jothi, Neesha Noraini, Ibrahim Halimah, Badioze Zaman M.H.M., Tahir Soudagar, Manzoore Elahi M. Ramesh, S. Shelare, Sagar Sharma, Shubham |
| author_sort | S.N., Nazrin |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Recent advancements in glass science have focused on optimizing optical and structural properties through the incorporation of rare oxide nanoparticles, such as lanthanum and gold oxide, to enhance transparency, refractive index, and band gap energy for photonic applications. Additionally, the integration of machine learning techniques in material design accelerates the prediction and optimisation of these properties, enabling more efficient discovery and development of advanced glass systems for optoelectronic and photonic devices. The meltquenching technique was employed to fabricate zinc borotellurite glasses doped with lanthanum oxide and gold oxide. The glass’s amorphous nature was confirmed through structural analysis, which detected absorption bands corresponding to TeO3, BO4, and BO3 units. A drop in molar volume with an increase in gold oxide quantity prompted a suggestion for structural reorganisation. Optical investigations indicated an increase in the optical band gap energy and a blue shift in the absorption edge. Four machine learning models—Random Forest, Gradient Boosting, XGBoost, and Artificial Neural Networks—were employed to forecast critical optical and physical parameters, such as refractive index, density, molar volume, optical band gap, and Urbach energy, to expedite the discovery process. This data-driven approach illustrates the transformative capabilities of machine learning in materials science, facilitating the swift modification of glass compositions to enhance optical |
| first_indexed | 2025-11-15T03:57:27Z |
| format | Article |
| id | ump-44999 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:57:27Z |
| publishDate | 2025 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-449992025-07-03T01:23:20Z http://umpir.ump.edu.my/id/eprint/44999/ Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications S.N., Nazrin Doroody, Camellia Liyana Adilla, Burhanuddin Jothi, Neesha Noraini, Ibrahim Halimah, Badioze Zaman M.H.M., Tahir Soudagar, Manzoore Elahi M. Ramesh, S. Shelare, Sagar Sharma, Shubham QA75 Electronic computers. Computer science QC Physics TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering TP Chemical technology Recent advancements in glass science have focused on optimizing optical and structural properties through the incorporation of rare oxide nanoparticles, such as lanthanum and gold oxide, to enhance transparency, refractive index, and band gap energy for photonic applications. Additionally, the integration of machine learning techniques in material design accelerates the prediction and optimisation of these properties, enabling more efficient discovery and development of advanced glass systems for optoelectronic and photonic devices. The meltquenching technique was employed to fabricate zinc borotellurite glasses doped with lanthanum oxide and gold oxide. The glass’s amorphous nature was confirmed through structural analysis, which detected absorption bands corresponding to TeO3, BO4, and BO3 units. A drop in molar volume with an increase in gold oxide quantity prompted a suggestion for structural reorganisation. Optical investigations indicated an increase in the optical band gap energy and a blue shift in the absorption edge. Four machine learning models—Random Forest, Gradient Boosting, XGBoost, and Artificial Neural Networks—were employed to forecast critical optical and physical parameters, such as refractive index, density, molar volume, optical band gap, and Urbach energy, to expedite the discovery process. This data-driven approach illustrates the transformative capabilities of machine learning in materials science, facilitating the swift modification of glass compositions to enhance optical Elsevier 2025 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/44999/1/Machine%20learning-driven%20prediction%20of%20optical%20and%20physical%20properties.pdf S.N., Nazrin and Doroody, Camellia and Liyana Adilla, Burhanuddin and Jothi, Neesha and Noraini, Ibrahim and Halimah, Badioze Zaman and M.H.M., Tahir and Soudagar, Manzoore Elahi M. and Ramesh, S. and Shelare, Sagar and Sharma, Shubham (2025) Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications. Ceramics International. pp. 1-14. ISSN 0272-8842. (In Press / Online First) (In Press / Online First) https://doi.org/10.1016/j.ceramint.2025.04.231 https://doi.org/10.1016/j.ceramint.2025.04.231 |
| spellingShingle | QA75 Electronic computers. Computer science QC Physics TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering TP Chemical technology S.N., Nazrin Doroody, Camellia Liyana Adilla, Burhanuddin Jothi, Neesha Noraini, Ibrahim Halimah, Badioze Zaman M.H.M., Tahir Soudagar, Manzoore Elahi M. Ramesh, S. Shelare, Sagar Sharma, Shubham Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications |
| title | Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications |
| title_full | Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications |
| title_fullStr | Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications |
| title_full_unstemmed | Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications |
| title_short | Machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications |
| title_sort | machine learning-driven prediction of optical and physical properties in lanthanum and gold-doped zinc borotellurite glasses for optoelectronic applications |
| topic | QA75 Electronic computers. Computer science QC Physics TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/44999/ http://umpir.ump.edu.my/id/eprint/44999/ http://umpir.ump.edu.my/id/eprint/44999/ http://umpir.ump.edu.my/id/eprint/44999/1/Machine%20learning-driven%20prediction%20of%20optical%20and%20physical%20properties.pdf |