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README.md

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+---
+language: en
+tags:
+- glass
+- materials-science
+- inorganic-chemistry
+- regression
+- scikit-learn
+- xgboost
+- property-prediction
+license: mit
+datasets:
+- epam/SciGlass
+---
+
+# Vitreos 🔬
+
+**Glass property prediction from oxide composition using machine learning.**
+
+Developed by [Doruk Doğular](https://github.com/dorukdogular) · [GitHub](https://github.com/dorukdogular/vitreos) · [Live Demo](https://vitreos.streamlit.app)
+
+## Models
+
+| Property | Samples | R² | MAE |
+|---|---|---|---|
+| Tg | 76,377 | 0.85 | 44 K |
+| Density | 31,173 | 0.88 | 0.26 g/cm³ |
+| Refractive Index | 58,913 | 0.83 | 0.036 |
+| GFA | 11,858 | — | 69% acc |
+
+## Usage
+
+```python
+import joblib, json, numpy as np
+
+model = joblib.load("tg_regressor.pkl")
+with open("tg_features.json") as f:
+    features = json.load(f)
+
+comp = {f: 0.0 for f in features}
+comp["SiO2"] = 72.0
+comp["Na2O"] = 14.0
+comp["CaO"] = 9.0
+
+X = np.array([[comp[f] for f in features]])
+print(f"Tg: {model.predict(X)[0]:.1f} K")
+```
+
+## Dataset
+
+Trained on [SciGlass](https://github.com/epam/SciGlass) by EPAM Systems — 422,000+ inorganic glass compositions, ODbL license.
+
+## Author
+
+**Doruk Doğular** · [@dorukdogular](https://github.com/dorukdogular)
+
+## License
+MIT (model) · ODbL (dataset)