| atomic-number-classification-on-chili-100k | EdgeCNN | #1 | F1-score (Weighted): 0.572 +/- 0.017 |
| atomic-number-classification-on-chili-100k | GIN | #2 | F1-score (Weighted): 0.336 +/- 0.005 |
| atomic-number-classification-on-chili-100k | GraphUNet | #3 | F1-score (Weighted): 0.287 +/- 0.004 |
| atomic-number-classification-on-chili-100k | GCN | #4 | F1-score (Weighted): 0.275 +/- 0.002 |
| atomic-number-classification-on-chili-100k | GraphSAGE | #5 | F1-score (Weighted): 0.195 +/- 0.007 |
| atomic-number-classification-on-chili-100k | Most Frequent Class | #6 | F1-score (Weighted): 0.192 |
| atomic-number-classification-on-chili-100k | GAT | #7 | F1-score (Weighted): 0.192 +/- 0.000 |
| atomic-number-classification-on-chili-100k | PMLP | #8 | F1-score (Weighted): 0.191 +/- 0.000 |
| atomic-number-classification-on-chili-100k | Random | #9 | F1-score (Weighted): 0.015 +/- 0.000 |
| atomic-number-classification-on-chili-3k | EdgeCNN | #1 | F1-score (Weighted): 0.632 +/- 0.009 |
| atomic-number-classification-on-chili-3k | GIN | #2 | F1-score (Weighted): 0.587 +/- 0.002 |
| atomic-number-classification-on-chili-3k | GraphUNet | #3 | F1-score (Weighted): 0.552 +/- 0.079 |
| atomic-number-classification-on-chili-3k | GCN | #4 | F1-score (Weighted): 0.496 +/- 0.001 |
| atomic-number-classification-on-chili-3k | GraphSAGE | #5 | F1-score (Weighted): 0.491 +/- 0.004 |
| atomic-number-classification-on-chili-3k | Most Frequent Class | #6 | F1-score (Weighted): 0.461 |
| atomic-number-classification-on-chili-3k | PMLP | #7 | F1-score (Weighted): 0.461 +/- 0.000 |
| atomic-number-classification-on-chili-3k | GAT | #8 | F1-score (Weighted): 0.461 +/- 0.000 |
| atomic-number-classification-on-chili-3k | Random | #9 | F1-score (Weighted): 0.016 +/- 0.000 |
| crystal-system-classification-on-chili-100k | Random | #1 | F1-score (Weighted): 0.168 +/- 0.014 |
| crystal-system-classification-on-chili-100k | PMLP | #2 | F1-score (Weighted): 0.124 +/- 0.036 |
| crystal-system-classification-on-chili-100k | GAT | #3 | F1-score (Weighted): 0.110 +/- 0.029 |
| crystal-system-classification-on-chili-100k | EdgeCNN | #4 | F1-score (Weighted): 0.072 +/- 0.047 |
| crystal-system-classification-on-chili-100k | GCN | #5 | F1-score (Weighted): 0.069 +/- 0.023 |
| crystal-system-classification-on-chili-100k | GIN | #6 | F1-score (Weighted): 0.069 +/- 0.040 |
| crystal-system-classification-on-chili-100k | GraphUNet | #7 | F1-score (Weighted): 0.068 +/- 0.006 |
| crystal-system-classification-on-chili-100k | GraphSAGE | #8 | F1-score (Weighted): 0.061 +/- 0.019 |
| crystal-system-classification-on-chili-100k | Most Frequent Class | #9 | F1-score (Weighted): 0.046 |
| crystal-system-classification-on-chili-3k | EdgeCNN | #1 | F1-score (Weighted): 0.657 +/- 0.196 |
| crystal-system-classification-on-chili-3k | GAT | #2 | F1-score (Weighted): 0.504 +/- 0.076 |
| crystal-system-classification-on-chili-3k | Most Frequent Class | #3 | F1-score (Weighted): 0.440 |
| crystal-system-classification-on-chili-3k | PMLP | #4 | F1-score (Weighted): 0.440 +/- 0.036 |
| crystal-system-classification-on-chili-3k | GIN | #5 | F1-score (Weighted): 0.438 +/- 0.004 |
| crystal-system-classification-on-chili-3k | GraphUNet | #6 | F1-score (Weighted): 0.431 +/- 0.014 |
| crystal-system-classification-on-chili-3k | GraphSAGE | #7 | F1-score (Weighted): 0.422 +/- 0.037 |
| crystal-system-classification-on-chili-3k | GCN | #8 | F1-score (Weighted): 0.367 +/- 0.127 |
| crystal-system-classification-on-chili-3k | Random | #9 | F1-score (Weighted): 0.191 +/- 0.008 |
| distance-regression-on-chili-100k | EdgeCNN | #1 | MSE: 0.030 +/- 0.001 |
| distance-regression-on-chili-100k | GraphSAGE | #2 | MSE: 0.064 +/- 0.001 |
| distance-regression-on-chili-100k | GraphUNet | #3 | MSE: 0.085 +/- 0.002 |
| distance-regression-on-chili-100k | GCN | #4 | MSE: 0.090 +/- 0.002 |
| distance-regression-on-chili-100k | GAT | #5 | MSE: 0.252 +/- 0.003 |
| distance-regression-on-chili-100k | Mean | #6 | MSE: 0.307 |
| distance-regression-on-chili-100k | PMLP | #7 | MSE: 0.486 +/- 0.014 |
| distance-regression-on-chili-100k | GIN | #8 | MSE: 0.491 +/- 0.038 |
| distance-regression-on-chili-3k | EdgeCNN | #1 | MSE: 0.015 +/- 0.001 |
| distance-regression-on-chili-3k | GraphSAGE | #2 | MSE: 0.055 +/- 0.002 |
| distance-regression-on-chili-3k | GraphUNet | #3 | MSE: 0.055 +/- 0.001 |
| distance-regression-on-chili-3k | GCN | #4 | MSE: 0.056 +/- 0.006 |
| distance-regression-on-chili-3k | Mean | #5 | MSE: 0.265 |
| distance-regression-on-chili-3k | GAT | #6 | MSE: 0.342 +/- 0.117 |
| distance-regression-on-chili-3k | PMLP | #7 | MSE: 0.359 +/- 0.017 |
| distance-regression-on-chili-3k | GIN | #8 | MSE: 0.464 +/- 0.005 |
| position-regression-on-chili-100k | GraphUNet | #1 | Positional MAE: 14.824 +/- 0.315 |
| position-regression-on-chili-100k | Mean | #2 | Positional MAE: 16.336 |
| position-regression-on-chili-100k | GCN | #3 | Positional MAE: 16.336 +/- 0.000 |
| position-regression-on-chili-100k | PMLP | #4 | Positional MAE: 16.336 +/- 0.000 |
| position-regression-on-chili-100k | GAT | #5 | Positional MAE: 16.336 +/- 0.000 |
| position-regression-on-chili-100k | GIN | #6 | Positional MAE: 16.336 +/- 0.000 |
| position-regression-on-chili-100k | EdgeCNN | #7 | Positional MAE: 16.336 +/- 0.000 |
| position-regression-on-chili-100k | GraphSAGE | #8 | Positional MAE: 16.337 +/- 0.000 |
| position-regression-on-chili-3k | GraphUNet | #1 | Positional MAE: 14.765 +/- 0.395 |
| position-regression-on-chili-3k | Mean | #2 | Positional MAE: 16.575 |
| position-regression-on-chili-3k | GCN | #3 | Positional MAE: 16.575 +/- 0.000 |
| position-regression-on-chili-3k | PMLP | #4 | Positional MAE: 16.575 +/- 0.000 |
| position-regression-on-chili-3k | GraphSAGE | #5 | Positional MAE: 16.575 +/- 0.000 |
| position-regression-on-chili-3k | GAT | #6 | Positional MAE: 16.575 +/- 0.000 |
| position-regression-on-chili-3k | GIN | #7 | Positional MAE: 16.575 +/- 0.000 |
| position-regression-on-chili-3k | EdgeCNN | #8 | Positional MAE: 16.575 +/- 0.000 |
| saxs-regression-on-chili-100k | PMLP | #1 | MSE: 0.003 +/- 0.000 |
| saxs-regression-on-chili-100k | EdgeCNN | #2 | MSE: 0.007 +/- 0.009 |
| saxs-regression-on-chili-100k | GAT | #3 | MSE: 0.009 +/- 0.000 |
| saxs-regression-on-chili-100k | GraphUNet | #4 | MSE: 0.009 +/- 0.000 |
| saxs-regression-on-chili-100k | GIN | #5 | MSE: 0.009 +/- 0.000 |
| saxs-regression-on-chili-100k | GCN | #6 | MSE: 0.010 +/- 0.000 |
| saxs-regression-on-chili-100k | GraphSAGE | #7 | MSE: 0.011 +/- 0.002 |
| saxs-regression-on-chili-100k | Mean | #8 | MSE: 0.038 |
| saxs-regression-on-chili-3k | EdgeCNN | #1 | MSE: 0.006 +/- 0.004 |
| saxs-regression-on-chili-3k | GCN | #2 | MSE: 0.008 +/- 0.000 |
| saxs-regression-on-chili-3k | GraphSAGE | #3 | MSE: 0.008 +/- 0.001 |
| saxs-regression-on-chili-3k | GAT | #4 | MSE: 0.008 +/- 0.000 |
| saxs-regression-on-chili-3k | GraphUNet | #5 | MSE: 0.008 +/- 0.000 |
| saxs-regression-on-chili-3k | GIN | #6 | MSE: 0.008 +/- 0.000 |
| saxs-regression-on-chili-3k | PMLP | #7 | MSE: 0.022 +/- 0.025 |
| saxs-regression-on-chili-3k | Mean | #8 | MSE: 0.037 |
| space-group-classification-on-chili-100k | EdgeCNN | #1 | F1-score (Weighted): 0.158 +/- 0.035 |
| space-group-classification-on-chili-100k | PMLP | #2 | F1-score (Weighted): 0.047 +/- 0.012 |
| space-group-classification-on-chili-100k | GraphSAGE | #3 | F1-score (Weighted): 0.044 +/- 0.002 |
| space-group-classification-on-chili-100k | GAT | #4 | F1-score (Weighted): 0.044 +/- 0.001 |
| space-group-classification-on-chili-100k | GCN | #5 | F1-score (Weighted): 0.043 +/- 0.001 |
| space-group-classification-on-chili-100k | GraphUNet | #6 | F1-score (Weighted): 0.043 +/- 0.000 |
| space-group-classification-on-chili-100k | GIN | #7 | F1-score (Weighted): 0.043 +/- 0.000 |
| space-group-classification-on-chili-100k | Most Frequent Class | #8 | F1-score (Weighted): 0.010 |
| space-group-classification-on-chili-100k | Random | #9 | F1-score (Weighted): 0.002 +/- 0.001 |
| space-group-classification-on-chili-3k | EdgeCNN | #1 | F1-score (Weighted): 0.733 +/- 0.207 |
| space-group-classification-on-chili-3k | GraphSAGE | #2 | F1-score (Weighted): 0.151 +/- 0.045 |
| space-group-classification-on-chili-3k | PMLP | #3 | F1-score (Weighted): 0.135 +/- 0.006 |
| space-group-classification-on-chili-3k | GIN | #4 | F1-score (Weighted): 0.125 +/- 0.026 |
| space-group-classification-on-chili-3k | GAT | #5 | F1-score (Weighted): 0.113 +/- 0.013 |
| space-group-classification-on-chili-3k | Most Frequent Class | #6 | F1-score (Weighted): 0.108 |
| space-group-classification-on-chili-3k | GCN | #7 | F1-score (Weighted): 0.099 +/- 0.019 |
| space-group-classification-on-chili-3k | GraphUNet | #8 | F1-score (Weighted): 0.095 +/- 0.036 |
| space-group-classification-on-chili-3k | Random | #9 | F1-score (Weighted): 0.009 +/- 0.008 |
| x-ray-pdf-regression-on-chili-100k | Mean | #1 | MSE: 0.007 |
| x-ray-pdf-regression-on-chili-100k | EdgeCNN | #2 | MSE: 0.012 +/- 0.000 |
| x-ray-pdf-regression-on-chili-100k | PMLP | #3 | MSE: 0.013 +/- 0.000 |
| x-ray-pdf-regression-on-chili-100k | GAT | #4 | MSE: 0.013 +/- 0.000 |
| x-ray-pdf-regression-on-chili-100k | GraphUNet | #5 | MSE: 0.013 +/- 0.000 |
| x-ray-pdf-regression-on-chili-100k | GIN | #6 | MSE: 0.013 +/- 0.000 |
| x-ray-pdf-regression-on-chili-100k | GCN | #7 | MSE: 0.014 +/- 0.000 |
| x-ray-pdf-regression-on-chili-100k | GraphSAGE | #8 | MSE: 0.037 +/- 0.026 |
| x-ray-pdf-regression-on-chili-3k | Mean | #1 | MSE: 0.008 |
| x-ray-pdf-regression-on-chili-3k | EdgeCNN | #2 | MSE: 0.011 +/- 0.000 |
| x-ray-pdf-regression-on-chili-3k | GCN | #3 | MSE: 0.012 +/- 0.000 |
| x-ray-pdf-regression-on-chili-3k | PMLP | #4 | MSE: 0.012 +/- 0.000 |
| x-ray-pdf-regression-on-chili-3k | GraphSAGE | #5 | MSE: 0.012 +/- 0.000 |
| x-ray-pdf-regression-on-chili-3k | GraphUNet | #6 | MSE: 0.012 +/- 0.000 |
| x-ray-pdf-regression-on-chili-3k | GAT | #7 | MSE: 0.029 +/- 0.030 |
| xrd-regression-on-chili-100k | EdgeCNN | #1 | MSE: 0.006 +/- 0.000 |
| xrd-regression-on-chili-100k | PMLP | #2 | MSE: 0.008 +/- 0.001 |
| xrd-regression-on-chili-100k | GCN | #3 | MSE: 0.009 +/- 0.000 |
| xrd-regression-on-chili-100k | GraphUNet | #4 | MSE: 0.009 +/- 0.000 |
| xrd-regression-on-chili-100k | GIN | #5 | MSE: 0.009 +/- 0.000 |
| xrd-regression-on-chili-100k | GraphSAGE | #6 | MSE: 0.018 +/- 0.014 |
| xrd-regression-on-chili-100k | Mean | #7 | MSE: 0.021 |
| xrd-regression-on-chili-100k | GAT | #8 | MSE: 0.108 +/- 0.172 |
| xrd-regression-on-chili-3k | EdgeCNN | #1 | MSE: 0.008 +/- 0.001 |
| xrd-regression-on-chili-3k | GCN | #2 | MSE: 0.010 +/- 0.000 |
| xrd-regression-on-chili-3k | PMLP | #3 | MSE: 0.010 +/- 0.000 |
| xrd-regression-on-chili-3k | GraphSAGE | #4 | MSE: 0.010 +/- 0.000 |
| xrd-regression-on-chili-3k | GAT | #5 | MSE: 0.010 +/- 0.000 |
| xrd-regression-on-chili-3k | GraphUNet | #6 | MSE: 0.010 +/- 0.000 |
| xrd-regression-on-chili-3k | Mean | #7 | MSE: 0.017 |