| --- |
| license: mit |
| tags: |
| - crystal-generation |
| - diffusion-model |
| - materials-science |
| - probe-gradient-guidance |
| library_name: pytorch |
| --- |
| |
| # Crystalite Balanced 100K (Production) |
|
|
| Crystalite checkpoint trained for 100K steps on a balanced 32K subset of Alex-MP-20 with 35% insulators (vs 2.1% in the full dataset). This is the production model for guided crystal generation. |
|
|
| **Architecture**: 67.8M-parameter Diffusion Transformer with subatomic tokenizer and GEM attention bias ([Crystalite](https://arxiv.org/abs/2604.02270), Hadzi Veljkovic et al.). |
|
|
| ## Results at w=3 (production operating point) |
|
|
| | Metric | Value | |
| |---|---| |
| | In-window rate (4-6 eV) | 42.6% | |
| | Lattice validity | 100% | |
| | Geometry validity | 99.6% | |
| | Compositional uniqueness | 78% | |
| | Metal fraction | 0.2% | |
|
|
| Formation energy probe AUROC: 0.990. Band gap probe AUROC: ~0.95. |
|
|
| ## Multi-constraint generation |
|
|
| Hybrid gradient steering + token masking produces: 100% refractory, 0% cobalt/nickel, 100% insulator, 30% in target window. |
|
|
| ## 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) |
|
|
