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Scandium-Dataset

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Model Leaderboard — Scandium-Dataset v1.0.0

Results on the four frozen benchmark splits. All models use the same train/val/test indices.

Composition-Only Baselines (RF + Ridge Ensemble)

Feature representation: Bag-of-elements (89-element fraction vector), StandardScaler-normalized.

Model: 200-tree RandomForest (max_depth=20) + Ridge(α=1.0), prediction = 0.5×RF + 0.5×Ridge.

Full Dataset (all tiers)

Split FE MAE FE RMSE FE R² EaH MAE EaH RMSE EaH R² BG MAE BG RMSE BG R² N (test)
random_80_10_10 0.3919 0.5717 0.7906 0.2244 0.4137 0.3246 0.4943 0.8633 0.5502 39,940
composition_held_out 0.3897 0.5474 0.8009 0.2226 0.3760 0.3825 0.4973 0.8608 0.5494 26,338
family_held_out 0.7387 0.8322 −1.5562 0.1689 0.4963 −0.0212 1.1045 1.2641 0.1378 583
chemistry_held_out 1.6565 1.8289 −2.5386 0.3551 0.4959 −0.9206 1.9662 2.4611 −0.0893 12,675

Gold Tier Only

Split FE MAE FE RMSE FE R² EaH MAE EaH RMSE EaH R² BG MAE BG RMSE BG R² N (test)
random_80_10_10 0.2984 0.4241 0.8543 0.0235 0.0280 0.2161 0.7621 1.0766 0.5990 14,721
composition_held_out 0.3062 0.4389 0.8378 0.0236 0.0280 0.2381 0.7719 1.0907 0.5958 9,453
family_held_out 0.4580 0.4953 −3.4812 0.0190 0.0237 0.2592 0.9426 1.1103 0.2920 507
chemistry_held_out 1.3838 1.5222 −1.8731 0.0274 0.0324 −0.0667 2.1287 2.7238 −0.3350 9,178

Key Findings

  1. Gold-tier training consistently beats full-dataset training on FE — MAE drops from 0.392 → 0.298 on random split, and from 1.657 → 1.384 on chemistry held-out. The tiering system demonstrably improves data quality for model training.

  2. EaH is dramatically better on Gold (0.023 vs 0.224 MAE) — because Gold excludes JARVIS entries (which lack EaH) and filters OQMD EaH outliers.

  3. Band gap is harder on Gold — likely because Gold contains fewer metallic entries (band gap = 0), shifting the distribution away from the population mean.

  4. Chemistry held-out is the hardest split — halides are genuinely out-of-distribution for composition-only models. FE R² is negative for both configurations, indicating the model cannot generalize to unseen chemistries.

  5. Family held-out is also challenging for FE — with only 583 test entries and unseen families, the R² is negative for both tier configurations.

Next Baselines to Add

  • CGCNN (requires structure parsing)
  • MEGNet (requires structure parsing)
  • ALIGNN (requires graph construction)
  • CrabNet (attention-based composition encoding)