Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses
Chalcogenide glasses (ChGs) are a class of amorphous materials presenting remarkable mechanical, optical, and electrical properties, making them promising candidates for advanced photonic and optoelectronic applications. With the increasing integration of artificial intelligence in modern materials...
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MDPI AG
2025-04-01
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| author | Miruna-Ioana Belciu Alin Velea |
| author_facet | Miruna-Ioana Belciu Alin Velea |
| author_sort | Miruna-Ioana Belciu |
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| description | Chalcogenide glasses (ChGs) are a class of amorphous materials presenting remarkable mechanical, optical, and electrical properties, making them promising candidates for advanced photonic and optoelectronic applications. With the increasing integration of artificial intelligence in modern materials design, we are able to systematically select, prepare, and optimize appropriate compositions for desired applications in a manner that was unachievable before. This study employs various machine learning models to reliably predict the refractive index at 20 °C using a small dataset of 541 samples extracted from the SciGlass database. The input for the algorithms consists of a selected set of physico-chemical features computed for the chemical composition of each entry. Additionally, these algorithms served as inner models for an ensemble logistic regression estimator that achieved a superior <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> value of 0.8985. SHAP feature analysis of the second-best model, CatBoostRegressor (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.8920), revealed the importance of elemental density, atomic weight, ground state atomic gap, and fraction of p valence electrons in tuning the value of the refractive index of a chalcogenide compound. |
| format | Article |
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| institution | DOAJ |
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| publishDate | 2025-04-01 |
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| spelling | doaj-art-02518d68fe284768a35cbc87f29cfea62025-08-20T03:13:48ZengMDPI AGMolecules1420-30492025-04-01308174510.3390/molecules30081745Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide GlassesMiruna-Ioana Belciu0Alin Velea1National Institute of Materials Physics, Atomistilor 405A, 077125 Magurele, RomaniaNational Institute of Materials Physics, Atomistilor 405A, 077125 Magurele, RomaniaChalcogenide glasses (ChGs) are a class of amorphous materials presenting remarkable mechanical, optical, and electrical properties, making them promising candidates for advanced photonic and optoelectronic applications. With the increasing integration of artificial intelligence in modern materials design, we are able to systematically select, prepare, and optimize appropriate compositions for desired applications in a manner that was unachievable before. This study employs various machine learning models to reliably predict the refractive index at 20 °C using a small dataset of 541 samples extracted from the SciGlass database. The input for the algorithms consists of a selected set of physico-chemical features computed for the chemical composition of each entry. Additionally, these algorithms served as inner models for an ensemble logistic regression estimator that achieved a superior <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> value of 0.8985. SHAP feature analysis of the second-best model, CatBoostRegressor (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.8920), revealed the importance of elemental density, atomic weight, ground state atomic gap, and fraction of p valence electrons in tuning the value of the refractive index of a chalcogenide compound.https://www.mdpi.com/1420-3049/30/8/1745chalcogenide glassesrefractive indexensemble learningsmall data |
| spellingShingle | Miruna-Ioana Belciu Alin Velea Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses Molecules chalcogenide glasses refractive index ensemble learning small data |
| title | Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses |
| title_full | Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses |
| title_fullStr | Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses |
| title_full_unstemmed | Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses |
| title_short | Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses |
| title_sort | ensemble machine learning for the prediction and understanding of the refractive index in chalcogenide glasses |
| topic | chalcogenide glasses refractive index ensemble learning small data |
| url | https://www.mdpi.com/1420-3049/30/8/1745 |
| work_keys_str_mv | AT mirunaioanabelciu ensemblemachinelearningforthepredictionandunderstandingoftherefractiveindexinchalcogenideglasses AT alinvelea ensemblemachinelearningforthepredictionandunderstandingoftherefractiveindexinchalcogenideglasses |