license: mit
tags:
- crystal-generation
- diffusion-model
- materials-science
- probe-gradient-guidance
library_name: pytorch
Crystalite 10K (Alex-MP-20)
Crystalite checkpoint trained for 10K steps on the full Alex-MP-20 dataset (540K structures, 97.9% metals). This is the diversity-optimized model used for the Pareto sweep experiments.
Architecture: 67.8M-parameter Diffusion Transformer with subatomic tokenizer and GEM attention bias (Crystalite, Hadzi Veljkovic et al.).
Key results with probe-gradient guidance
| Guidance weight | In-window (4-6 eV) | Uniqueness | Metal % |
|---|---|---|---|
| 0 (baseline) | 0.1% | 99.7% | 96.9% |
| 10 | 31.8% | 99.7% | 0.1% |
| 15 | 33.7% | 99.6% | 0.0% |
Every guidance weight Pareto-dominates the baseline. 18,432 structures across 6 weights, 3 seeds, 1,024 per batch. No mode collapse.
Band gap probe AUROC: 0.957 (256 parameters, trained on atom-mean hidden states).
Usage
Requires the Crystalite codebase and probe-gradient-guidance scripts.
from scripts.train_probe import load_model
model = load_model("final.pt", device="cuda")
Links
- Blog post: Scaling Test-Time Verification for Novel Materials
- Code: Dynamical-Systems-Research/probe-gradient-guidance
- Crystalite paper: arXiv:2604.02270
Used In
This checkpoint was used as an upstream generation asset in the open-world environment pipeline for Training Scientific Judgment with Verified Environments for Autonomous Science.
- Scientific judgment blog post: Training Scientific Judgment
- Public repo: Dynamical-Systems-Research/training-scientific-judgment
- Paper PDF: Training Scientific Judgment with Verified Environments for Autonomous Science