| # Benchmark |
|
|
| ## Overview |
|
|
| The Scandium Benchmark defines a standard evaluation protocol for ML models |
| trained on the Scandium Dataset. All models report results on the same frozen |
| splits using the same metrics, enabling fair comparison. |
|
|
| ## Splits |
|
|
| Four split types, all formula-grouped (no same formula across splits), seed=42: |
|
|
| | Split | Train | Val | Test | Tests | |
| |-------|-------|-----|------|-------| |
| | `random_80_10_10` | 200,122 | 26,670 | 39,940 | Basic generalization | |
| | `composition_held_out` | 213,383 | 27,011 | 26,338 | No formula overlap | |
| | `family_held_out` | 260,984 | 5,165 | 583 | Cross-family generalization | |
| | `chemistry_held_out` | 227,384 | 26,673 | 12,675 | OOD (all halides as test) | |
|
|
| ## Metrics |
|
|
| ### Required |
| - MAE, RMSE, R² for FE, EaH, BG |
| - Report per split, per family, per source |
|
|
| ### Recommended |
| - ECE (Expected Calibration Error) |
| - Spearman ρ (ranking correlation) |
| - F1 for stability classification (EaH < 25 meV) |
|
|
| ## Baseline Results |
|
|
| ### Composition-Only Baseline (RF + Ridge on bag-of-elements features) |
|
|
| Results on the random 80/10/10 split, comparing training on the full dataset vs |
| Gold-tier only. See [MODEL_LEADERBOARD.md](../MODEL_LEADERBOARD.md) for complete results. |
|
|
| #### Full Dataset |
|
|
| | Split | FE MAE | FE RMSE | FE R² | EaH MAE | EaH RMSE | BG MAE | BG RMSE | |
| |-------|--------|---------|-------|---------|----------|--------|---------| |
| | random_80_10_10 | 0.392 | 0.572 | 0.791 | 0.224 | 0.414 | 0.494 | 0.863 | |
| | composition_held_out | 0.390 | 0.547 | 0.801 | 0.223 | 0.376 | 0.497 | 0.861 | |
| | family_held_out | 0.739 | 0.832 | −1.556 | 0.169 | 0.496 | 1.105 | 1.264 | |
| | chemistry_held_out | 1.657 | 1.829 | −2.539 | 0.355 | 0.496 | 1.966 | 2.461 | |
| |
| #### Gold Tier Only |
| |
| | Split | FE MAE | FE RMSE | FE R² | EaH MAE | EaH RMSE | BG MAE | BG RMSE | |
| |-------|--------|---------|-------|---------|----------|--------|---------| |
| | random_80_10_10 | 0.298 | 0.424 | 0.854 | 0.024 | 0.028 | 0.762 | 1.077 | |
| | composition_held_out | 0.306 | 0.439 | 0.838 | 0.024 | 0.028 | 0.772 | 1.091 | |
| | family_held_out | 0.458 | 0.495 | −3.481 | 0.019 | 0.024 | 0.943 | 1.110 | |
| | chemistry_held_out | 1.384 | 1.522 | −1.873 | 0.027 | 0.032 | 2.129 | 2.724 | |
|
|
| ### Key Takeaway |
|
|
| **Gold-tier training consistently beats full-dataset training.** FE MAE drops |
| from 0.392 → 0.298 on the random split and from 1.657 → 1.384 on the |
| chemistry held-out split, despite having ~3× fewer training examples. This |
| directly validates the tiering system: higher-quality data produces better |
| models even with less data. |
|
|
| ## Evaluation Runner |
|
|
| ```bash |
| # Evaluate predictions on all splits |
| python benchmark/evaluate.py \ |
| --splits random_80_10_10 composition_held_out \ |
| --predictions predictions.json \ |
| --model-name my_model |
| |
| # Run baseline |
| python benchmark/evaluate.py --baseline mean |
| python benchmark/evaluate.py --baseline median |
| ``` |
|
|
| ## Leaderboard |
|
|
| See [MODEL_LEADERBOARD.md](../MODEL_LEADERBOARD.md) for complete results. |
|
|
