Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning.
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| Title: | Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning. |
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| Authors: | Xie, Yuhao1,2 (AUTHOR), Wang, Xiangfu1,2,3 (AUTHOR) xfwang@njupt.edu.cn |
| Source: | Nanomaterials (2079-4991). Jun2025, Vol. 15 Issue 11, p860. 16p. |
| Subjects: | Machine learning, Liquidus temperature, Support vector machines, K-nearest neighbor classification, Ternary system |
| Abstract: | Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optical properties of oxyfluoride glass, namely glass transition temperature, density, Abbe number, liquidus temperature, thermal expansion coefficient, and refractive index. We perform SHAP analysis on the constructed machine learning model to explain the effects of different components on the properties. Based on the trained machine learning models, we developed several ternary system prediction maps that can effectively predict the properties of glasses composed of different proportions of components. This study provides a method to design new oxyfluoride glasses only knowing the components of glasses, which is instructive for the development of new types of oxyfluoride glasses as well as for computer-aided reverse design. [ABSTRACT FROM AUTHOR] |
| Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 185868462 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xie%2C+Yuhao%22">Xie, Yuhao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Xiangfu%22">Wang, Xiangfu</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> xfwang@njupt.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Nanomaterials+%282079-4991%29%22">Nanomaterials (2079-4991)</searchLink>. Jun2025, Vol. 15 Issue 11, p860. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Liquidus+temperature%22">Liquidus temperature</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Ternary+system%22">Ternary system</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optical properties of oxyfluoride glass, namely glass transition temperature, density, Abbe number, liquidus temperature, thermal expansion coefficient, and refractive index. We perform SHAP analysis on the constructed machine learning model to explain the effects of different components on the properties. Based on the trained machine learning models, we developed several ternary system prediction maps that can effectively predict the properties of glasses composed of different proportions of components. This study provides a method to design new oxyfluoride glasses only knowing the components of glasses, which is instructive for the development of new types of oxyfluoride glasses as well as for computer-aided reverse design. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/nano15110860 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 860 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Liquidus temperature Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Ternary system Type: general Titles: – TitleFull: Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xie, Yuhao – PersonEntity: Name: NameFull: Wang, Xiangfu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20794991 Numbering: – Type: volume Value: 15 – Type: issue Value: 11 Titles: – TitleFull: Nanomaterials (2079-4991) Type: main |
| ResultId | 1 |
