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README.md

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+---
+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)