metadata
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 · GitHub · Live Website
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 |
Performance
Usage
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 by EPAM Systems — 422,000+ inorganic glass compositions, ODbL license.
Author
Doruk Doğular · @dorukdogular
License
MIT (model) · ODbL (dataset)