| --- |
| 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](https://arxiv.org/abs/2604.02270), 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](https://github.com/joshrosie/crystalite) codebase and [probe-gradient-guidance](https://github.com/Dynamical-Systems-Research/probe-gradient-guidance) scripts. |
|
|
| ```python |
| from scripts.train_probe import load_model |
| model = load_model("final.pt", device="cuda") |
| ``` |
|
|
| ## Links |
|
|
| - **Blog post**: [Scaling Test-Time Verification for Novel Materials](https://dynamicalsystems.ai/blog/scaling-test-time-verification) |
| - **Code**: [Dynamical-Systems-Research/probe-gradient-guidance](https://github.com/Dynamical-Systems-Research/probe-gradient-guidance) |
| - **Crystalite paper**: [arXiv:2604.02270](https://arxiv.org/abs/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](https://dynamicalsystems.ai/blog/training-scientific-judgment) |
| - **Public repo**: [Dynamical-Systems-Research/training-scientific-judgment](https://github.com/Dynamical-Systems-Research/training-scientific-judgment) |
| - **Paper PDF**: [Training Scientific Judgment with Verified Environments for Autonomous Science](https://github.com/Dynamical-Systems-Research/training-scientific-judgment/blob/main/paper/training-scientific-judgment.pdf) |
|
|
