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Atomic Number Classification On Chili 3K

Metrics

F1-score (Weighted)

Results

Performance results of various models on this benchmark

Model Name
F1-score (Weighted)
Paper TitleRepository
Random0.016 +/- 0.000CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
GIN0.587 +/- 0.002CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
PMLP0.461 +/- 0.000CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
EdgeCNN0.632 +/- 0.009CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
GCN0.496 +/- 0.001CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
GraphUNet0.552 +/- 0.079CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
Most Frequent Class0.461CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
GraphSAGE0.491 +/- 0.004CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
GAT0.461 +/- 0.000CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning-
0 of 9 row(s) selected.
Atomic Number Classification On Chili 3K | SOTA | HyperAI