Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification
Abstract Chalcogenide glasses, renowned for their exceptional optoelectronic properties, have become the material of choice for numerous microelectronic and optical devices. With the rapid development of artificial intelligence technologies in the field of materials science, researchers have increas...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04391-9 |
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| author | Hui Li Pan Liu Shaoyun Liu Bin Yang |
| author_facet | Hui Li Pan Liu Shaoyun Liu Bin Yang |
| author_sort | Hui Li |
| collection | DOAJ |
| description | Abstract Chalcogenide glasses, renowned for their exceptional optoelectronic properties, have become the material of choice for numerous microelectronic and optical devices. With the rapid development of artificial intelligence technologies in the field of materials science, researchers have increasingly incorporated machine learning (ML) methods to accelerate the exploration of composition–structure–property relationships in chalcogenide glasses. However, traditional ML methods, which predominantly rely on single-property modelling, are often inadequate for addressing the practical demand for multiproperty collaborative optimization. To overcome this limitation, this study proposes a graph-based deep learning approach and develops a novel model for the efficient prediction of key properties of chalcogenide glasses. Furthermore, relevant data were collected from the publicly available SciGlass database to construct an experimental dataset, and the performance of the developed model was systematically evaluated. The experimental results demonstrate the model’s remarkable stability and predictive performance, highlighting its potential application value in the design and development of chalcogenide glasses. |
| format | Article |
| id | doaj-art-1a8d6e13a9a94e0199f364479ddac8bd |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1a8d6e13a9a94e0199f364479ddac8bd2025-08-20T03:10:29ZengNature PortfolioScientific Reports2045-23222025-06-011511810.1038/s41598-025-04391-9Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classificationHui Li0Pan Liu1Shaoyun Liu2Bin Yang3Kunming University of Science and TechnologyShandong Humon Smelting Co., Ltd.Shandong Humon Smelting Co., Ltd.Kunming University of Science and TechnologyAbstract Chalcogenide glasses, renowned for their exceptional optoelectronic properties, have become the material of choice for numerous microelectronic and optical devices. With the rapid development of artificial intelligence technologies in the field of materials science, researchers have increasingly incorporated machine learning (ML) methods to accelerate the exploration of composition–structure–property relationships in chalcogenide glasses. However, traditional ML methods, which predominantly rely on single-property modelling, are often inadequate for addressing the practical demand for multiproperty collaborative optimization. To overcome this limitation, this study proposes a graph-based deep learning approach and develops a novel model for the efficient prediction of key properties of chalcogenide glasses. Furthermore, relevant data were collected from the publicly available SciGlass database to construct an experimental dataset, and the performance of the developed model was systematically evaluated. The experimental results demonstrate the model’s remarkable stability and predictive performance, highlighting its potential application value in the design and development of chalcogenide glasses.https://doi.org/10.1038/s41598-025-04391-9Chalcogenide glasses; Deep learning; Property prediction |
| spellingShingle | Hui Li Pan Liu Shaoyun Liu Bin Yang Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification Scientific Reports Chalcogenide glasses; Deep learning; Property prediction |
| title | Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification |
| title_full | Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification |
| title_fullStr | Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification |
| title_full_unstemmed | Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification |
| title_short | Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification |
| title_sort | deep learning assisted attribute prediction of chalcogenide glasses based on graph classification |
| topic | Chalcogenide glasses; Deep learning; Property prediction |
| url | https://doi.org/10.1038/s41598-025-04391-9 |
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