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)