---
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)