Predict how synthesizable an inorganic crystal is from its structure. Returns a Crystal-Likeness Score (CLscore) in [0, 1] using Jang et al.'s positive–unlabeled CGCNN ensemble — a soft prior complementary to energy-above-hull filters.
Predict how synthesizable an inorganic crystal is from its structure. Returns a Crystal-Likeness Score (CLscore) in [0, 1] using Jang et al.'s positive–unlabeled CGCNN ensemble — a soft prior complementary to energy-above-hull filters.
PU-CGCNN validation: experimental crystals vs high-e_hull theoreticals
Spot-check of PU-CGCNN on 10 experimentally known vs 10 high-e_hull theoretical Materials Project structures. Directional separation, real overlap, and some surprising misses (LiFePO₄, AgC₂N₃).
Will it actually form? Predicting crystal synthesizability with PU-CGCNN
An explainer for the new Crystal-Likeness Score (PU-CGCNN) service: what the CLscore means, how positive–unlabeled learning works, and when to use it in a screening pipeline.
PU-CGCNN validation: experimental crystals vs high-e_hull theoreticals
Spot-check of PU-CGCNN on 10 experimentally known vs 10 high-e_hull theoretical Materials Project structures. Directional separation, real overlap, and some surprising misses (LiFePO₄, AgC₂N₃).
Will it actually form? Predicting crystal synthesizability with PU-CGCNN
An explainer for the new Crystal-Likeness Score (PU-CGCNN) service: what the CLscore means, how positive–unlabeled learning works, and when to use it in a screening pipeline.