Run the Jang et al. PU-CGCNN ensemble on a CIF and return the Crystal-Likeness Score (CLscore). Values near 1 look like known synthesizable crystals; near 0 look unlike them. Typical screening thresholds in the literature are around 0.5–0.7.
Run the Jang et al. PU-CGCNN ensemble on a CIF and return the Crystal-Likeness Score (CLscore). Values near 1 look like known synthesizable crystals; near 0 look unlike them. Typical screening thresholds in the literature are around 0.5–0.7.
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Usage
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22 callsView historyPU-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.