An Interpretable XGBoost Framework for Predicting Oxide Glass Density
Accurately predicting glass density is crucial for designing novel materials. This study aims to develop a robust predictive model for the density of oxide glasses and, more importantly, to investigate how physically informed feature engineering can create accurate and interpretable models that reve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/15/8680 |
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