---
license: cc-by-nc-4.0
library_name: pytorch
tags:
  - crystal-structure-prediction
  - flow-matching
  - molecular-crystals
---

# PackFlow checkpoints

Curated PackFlow model weights from
[PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment](https://arxiv.org/abs/2602.20140).

Load them with the [`packflow`](https://github.com/learningmatter-mit/packflow) package (`packflow download` / `load_checkpoint`).

| File | Role |
|------|------|
| `packflow-2M/best_model.pt` | ~2M base model |
| `packflow-20M/best_model.pt` | ~20M base model |
| `packflow-ddp/best_model.pt` | ~60M canonical base model (`packflow-60M`) |
| `packflow-pa/best_model.pt` | GRPO preference-aligned finetune of the 60M model |

Training structures are **not** included. CSD refcodes for the splits are in the GitHub repository (`data/refcodes/`).

## License

The **source code** for PackFlow is released under an MIT License. However, since PackFlow was trained on data from [CCDC's Cambridge Structural Database](https://www.ccdc.cam.ac.uk/), the **model weights** are released under a [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/) (CC BY-NC 4.0) License. For commercial use of the model weights, please ensure that you have a proper [CCDC License](https://www.ccdc.cam.ac.uk/support-and-resources/licensing-information/).