Interpretable machine learning for understanding compositional and testing condition effects on refractive index, density, dielectric constant, and loss tangent of inorganic melts and glasses Article Swipe
Mohd Zaki
,
Jayadeva
,
N. M. Anoop Krishnan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fmats.2024.1412701
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fmats.2024.1412701
Artificial intelligence (AI) and machine learning (ML) have enabled property-targeted design of glasses. Several machine learning models and open-source tools in the literature allow researchers to predict the optical, physical, mechanical, and electrical properties of glasses as a function of their chemical compositions. However, these properties also depend on testing conditions. In this paper, we train machine learning models by considering composition and wavelength, temperature, and frequency to predict the refractive index, density, and the two electrical properties, i.e., dielectric constant and loss tangent of glasses, respectively. The predictions of trained models are explained using SHAP analysis, revealing that testing conditions, such as wavelength and temperature, interact majorly with network formers while predicting refractive index and density. In the case of electrical properties, network formers and frequency have the highest interactions, followed by network modifiers and intermediates, and hence govern predictions of dielectric constant and loss tangent. Overall, AI/ML models that can predict the properties of glasses as a function of their composition and testing conditions, coupled with SHAP plots, provide a practical tool to develop a range of glasses for application under varying conditions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmats.2024.1412701
- OA Status
- gold
- Cited By
- 7
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402576887
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4402576887Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmats.2024.1412701Digital Object Identifier
- Title
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Interpretable machine learning for understanding compositional and testing condition effects on refractive index, density, dielectric constant, and loss tangent of inorganic melts and glassesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-18Full publication date if available
- Authors
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Mohd Zaki, Jayadeva, N. M. Anoop KrishnanList of authors in order
- Landing page
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https://doi.org/10.3389/fmats.2024.1412701Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3389/fmats.2024.1412701Direct OA link when available
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Dielectric, Dissipation factor, Refractive index, Tangent, Materials science, Constant (computer programming), Wavelength, Function (biology), Machine learning, Mineralogy, Optics, Computer science, Mathematics, Chemistry, Physics, Optoelectronics, Geometry, Biology, Programming language, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.AI/ML | 148 |
| abstract_inverted_index.allow | 23 |
| abstract_inverted_index.hence | 138 |
| abstract_inverted_index.i.e., | 78 |
| abstract_inverted_index.index | 114 |
| abstract_inverted_index.range | 177 |
| abstract_inverted_index.their | 40, 161 |
| abstract_inverted_index.these | 44 |
| abstract_inverted_index.tools | 19 |
| abstract_inverted_index.train | 55 |
| abstract_inverted_index.under | 182 |
| abstract_inverted_index.using | 94 |
| abstract_inverted_index.while | 111 |
| abstract_inverted_index.depend | 47 |
| abstract_inverted_index.design | 10 |
| abstract_inverted_index.govern | 139 |
| abstract_inverted_index.index, | 71 |
| abstract_inverted_index.models | 16, 58, 91, 149 |
| abstract_inverted_index.paper, | 53 |
| abstract_inverted_index.plots, | 169 |
| abstract_inverted_index.Several | 13 |
| abstract_inverted_index.coupled | 166 |
| abstract_inverted_index.develop | 175 |
| abstract_inverted_index.enabled | 8 |
| abstract_inverted_index.formers | 110, 124 |
| abstract_inverted_index.glasses | 35, 156, 179 |
| abstract_inverted_index.highest | 129 |
| abstract_inverted_index.machine | 4, 14, 56 |
| abstract_inverted_index.majorly | 107 |
| abstract_inverted_index.network | 109, 123, 133 |
| abstract_inverted_index.predict | 26, 68, 152 |
| abstract_inverted_index.provide | 170 |
| abstract_inverted_index.tangent | 83 |
| abstract_inverted_index.testing | 49, 99, 164 |
| abstract_inverted_index.trained | 90 |
| abstract_inverted_index.varying | 183 |
| abstract_inverted_index.However, | 43 |
| abstract_inverted_index.Overall, | 147 |
| abstract_inverted_index.chemical | 41 |
| abstract_inverted_index.constant | 80, 143 |
| abstract_inverted_index.density, | 72 |
| abstract_inverted_index.density. | 116 |
| abstract_inverted_index.followed | 131 |
| abstract_inverted_index.function | 38, 159 |
| abstract_inverted_index.glasses, | 85 |
| abstract_inverted_index.glasses. | 12 |
| abstract_inverted_index.interact | 106 |
| abstract_inverted_index.learning | 5, 15, 57 |
| abstract_inverted_index.optical, | 28 |
| abstract_inverted_index.tangent. | 146 |
| abstract_inverted_index.analysis, | 96 |
| abstract_inverted_index.explained | 93 |
| abstract_inverted_index.frequency | 66, 126 |
| abstract_inverted_index.modifiers | 134 |
| abstract_inverted_index.physical, | 29 |
| abstract_inverted_index.practical | 172 |
| abstract_inverted_index.revealing | 97 |
| abstract_inverted_index.Artificial | 0 |
| abstract_inverted_index.dielectric | 79, 142 |
| abstract_inverted_index.electrical | 32, 76, 121 |
| abstract_inverted_index.literature | 22 |
| abstract_inverted_index.predicting | 112 |
| abstract_inverted_index.properties | 33, 45, 154 |
| abstract_inverted_index.refractive | 70, 113 |
| abstract_inverted_index.wavelength | 103 |
| abstract_inverted_index.application | 181 |
| abstract_inverted_index.composition | 61, 162 |
| abstract_inverted_index.conditions, | 100, 165 |
| abstract_inverted_index.conditions. | 50, 184 |
| abstract_inverted_index.considering | 60 |
| abstract_inverted_index.mechanical, | 30 |
| abstract_inverted_index.open-source | 18 |
| abstract_inverted_index.predictions | 88, 140 |
| abstract_inverted_index.properties, | 77, 122 |
| abstract_inverted_index.researchers | 24 |
| abstract_inverted_index.wavelength, | 63 |
| abstract_inverted_index.intelligence | 1 |
| abstract_inverted_index.temperature, | 64, 105 |
| abstract_inverted_index.compositions. | 42 |
| abstract_inverted_index.interactions, | 130 |
| abstract_inverted_index.respectively. | 86 |
| abstract_inverted_index.intermediates, | 136 |
| abstract_inverted_index.property-targeted | 9 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.87459046 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |