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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 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)