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Atomic Number Classification On Chili 100K

Métriques

F1-score (Weighted)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
F1-score (Weighted)
Paper TitleRepository
GAT0.192 +/- 0.000CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
PMLP0.191 +/- 0.000CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
GIN0.336 +/- 0.005CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
GraphSAGE0.195 +/- 0.007CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
GraphUNet0.287 +/- 0.004CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
GCN0.275 +/- 0.002CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
EdgeCNN0.572 +/- 0.017CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
Random0.015 +/- 0.000CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
Most Frequent Class0.192CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
0 of 9 row(s) selected.
Atomic Number Classification On Chili 100K | SOTA | HyperAI