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rajkumars47/cues-alex-nolemat-lowhull
cues-alex-nolemat-lowhull is a machine learning model from rajkumars47. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
The crystal-domain model behind the steering paper's cross-domain claims. Written to parity with the image setup (DiT-XL/2 + ResNet-50): architecture, training data, objective, release, and the exact metric definition…
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Updated Jul 1, 2026
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From the Hugging Face model README
The crystal-domain model behind the steering paper's cross-domain claims. Written to parity with the image setup (DiT-XL/2 + ResNet-50): architecture, training data, objective, release, and the exact metric definitions every cited number uses.
| Name | CUES (checkpoint alex_nolemat_lowhull, epoch 120) |
| Architecture | EdGPT encoder–decoder transformer |
| Size | encoder 6 layers / decoder 18 layers, model dim 768, ≈238 M parameters |
| Vocabulary | 16,000 (SentencePiece; model_sp.model, committed) |
| Generation-internal version id | d15_binrho_k7 (target encoding scheme) |
| Checkpoint | ed_ckpt_final.pt, 909 MB, sha256 9b8e4b2e…54177 (see ARTIFACTS.md) |
| Release | HuggingFace rajkumars47/cues-alex-nolemat-lowhull (fetched, not committed; fetch_checkpoint.sh) |
Conditioning / I/O. A prompt is "<elements> | <natoms> | <bin-tags>", where the bin-tags
condition the generation on coarse property bins:
bg-{vlow,low,mid,high,vhigh} — band-gap binhull-{vlow,low,mid,high} — energy-above-hull (stability) bink-{…} — dielectric constant bin · rho-{…} — density binThe decoder emits a structure encoding (anonymized formula + concrete formula, space group, and
coordination/near-neighbor geometry) that is parsed to an ASE Atoms (lattice + sites). Naked
prompts ("<elements> | <natoms> | ", no tags) are used for the distribution-steering experiments.
alex_nolemat_lowhull training set
(alex_nolemat_lowhull_dataset_train.csv, ≈225 k structures), i.e. Alexandria structures,
natoms < 20, restricted to a low energy-above-hull subset, with LeMat-Bulk overlap
excluded (nolemat) so LeMat-Bulk can serve as the novelty reference at eval time.source, target, id, origin, label, e_above_hull, in_lemat — source is the
prompt (elements | natoms | tags), target the structure encoding, origin = alex.Supervised next-token cross-entropy: encoder-decoder translation from (composition + property-bin tags) to the structure encoding. No RL/preference objective in the base CUES model. (A separate aggressive DPO/IPO novelty fine-tune is used only for the Claim 4.4 off-support demonstration; it is not the CUES generator.)
e_above_hull, eV/atom): a single MACE single-point
energy evaluated against a convex hull whose Materials-Project reference entries are
re-evaluated with the same MACE (self-consistent; cached per chemical system). stable =
eah ≤ 0; metastable = 0 < eah ≤ 0.1. (chem.stability.compute_e_above_hull.) Needs
MP_API_KEY + emmet-core. A 3-MLIP variant (orb _mpa + MACE + UMA, mean hull) is available
for cross-checks; use orb _mpa not _omat.n_total (no validity gate — validity is a separate
metric). SUN = stable ∧ novel ∧ unique; MSUN = metastable ∧ novel ∧ unique.
Novelty (vs LeMat-Bulk) and uniqueness via lemat-genbench SUNMetric.calculate_bv_sum, minus the
formal oxidation state). 0 = perfectly self-consistent; None for pure intermetallics (no anion).chem.validity.check_validity, one call).Scripts live in steering/crystals/ (mirrored from dielectric/eval/within_distribution_steering/):
generate.py --target T + score.py — Combined steering (Claim 2).experiments/scan_extremes.py, natural_pools.py, method_compare.py — the steering range and
the two target-hitting methods.scripts/claim1_survival_spectrum.py + claim1_survival_crystals.py — the Claim 1 carry-over
ladder and density view.scripts/plot_pred_vs_obs.py, dpo_logprob_fig.py, make_figures.py — the paper figures.review1/ — the reviewer-requested CIs, convergence, joint baselines, and ladder prediction.Reference data revisions (LeMat-Bulk, LeMat-Bulk-MLIP-Hull, UMA) are pinned in ARTIFACTS.md.
The model is fetched from HF and hash-verified; nothing irreproducible is committed.
All numbers in the crystal track use these exact scorers/versions (full freeze in
requirements.txt):
| scorer | package / model | version |
|---|---|---|
| stability (single-MACE e_above_hull) | mace-torch, foundation model mace-mp-0b3-medium | 0.3.15 |
| stability ensemble (C-E9) | MACE foundation models {mace-mp-0b3-medium, mace_mp medium, mace_mp small}, median | 0.3.15 |
3-MLIP ensemble (optional, .venv_genbench) | orb (orb_v3_conservative_inf_mpa) + MACE + UMA, mean hull | orb-models 0.7.0, fairchem-core 2.21.0 |
| novelty / uniqueness (SUN/MSUN) | lemat-genbench SUNMetric vs LeMat-Bulk | bundled (rev in ARTIFACTS.md) |
| BVS-GII | pymatgen BVAnalyzer + calculate_bv_sum (Brown–Altermatt) | pymatgen 2026.5.4 |
| validity (SMACT ∧ BVS ∧ min-dist) | chem.validity (SMACT + pymatgen) | SMACT 4.0.0, pymatgen 2026.5.4 |
| band gap (C-E3) | XGBoost composition DFT-band-gap surrogate (data/xgb_composition_dft_band_gap.json) + matminer features | xgboost 3.2.0, matminer 0.10.1 |
| MP hull references | Materials Project API + emmet-core, re-evaluated with the same MACE | emmet-core 0.87.0 |
Sample sizes / seeds / CI method (consolidated):
d96b827 before the C-E2 audit (08981f7);
prior on a correct 3-rung ordering = 1/6.