PackFlow

This repository is the code accompanying the paper PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment.

PackFlow predicts how a molecule packs into a crystal. Given a molecule's graph (its atoms and bonds — no coordinates), it samples full crystal structures: Cartesian coordinates for every atom plus the unit cell. This is a self-contained, uv-installable reproduction of the paper, covering the full pipeline: data preprocessing (from the CSD), base-model training (2M / 20M / 60M), GRPO post-training (the preference-aligned "PA" model), evaluation (with the Genarris and UMA baselines), and relaxation / lattice-energy analysis.

packflow/
├── packflow/                # importable package
│   ├── models/model.py      # Cartesian crystal transformer + flow matching (canonical architecture)
│   ├── data/                # dataset / datamodule / collate
│   ├── processing/          # mmCIF/SMILES -> training tensors
│   ├── utils/               # crystal / lattice helpers + coordinate-free graph builder
│   ├── training/            # compact trainer + TrainConfig + train()
│   ├── grpo/                # GRPO post-training (trainer, sampler, model loader)
│   ├── evaluation/          # crystal-matching metrics + evaluator + evaluate()
│   ├── relaxation/          # UMA energy/forces, relaxation, lattice energy (Figure 5 backbone)
│   ├── baselines/           # Genarris wrapper
│   ├── inference/           # high-level load_checkpoint() / predict() / run_inference()
│   ├── checkpoints/         # model_zoo.json (the .pt files are fetched from Hugging Face)
│   ├── config.py            # central paths / env vars
│   ├── checkpoints.py       # zoo resolution + HF download
│   └── cli.py               # `packflow` command-line interface
├── examples/                # runnable usage examples
├── tests/                   # pytest suite
├── external/                # pinned git submodules: Crystal_Math, fairchem (UMA), Genarris
├── preprocessing/           # our Crystal_Math edits (overlay) + split scripts (CSD license)
└── data/refcodes/           # CSD refcodes for train/val/test + blind test (NOT the dataset)

Installation

PackFlow uses uv.

git clone <this-repo> packflow && cd packflow

# Pinned external code: Crystal_Math (preprocessing), fairchem (UMA), Genarris (baseline)
git submodule update --init --recursive

uv sync                       # core: training, sampling/inference, evaluation metrics

# Optional extras
uv pip install -e ".[grpo]"   # GRPO post-training (wandb, torch-scatter)

# UMA / fairchem (relaxation, lattice energy, GRPO reward) — installed from the submodule:
uv pip install -e external/fairchem/packages/fairchem-core

Python: 3.10 is recommended. The CSD preprocessing step needs Python 3.9 and a proprietary CCDC license (see preprocessing/README.md); it is only needed to rebuild the training dataset.

Quickstart: pack an arbitrary molecule

PackFlow can pack a molecule it has never seen, given only its graph. The easiest way is a SMILES string:

from packflow import load_checkpoint, predict, write_cif

model = load_checkpoint("packflow-60M", device="cpu")   # downloaded from HF on first use

# Benzene: 6 carbons in a ring (hydrogens implicit). SMILES sets the bond orders.
results = predict(
    elements=["C"] * 6,
    bond_index=[(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 0)],
    smiles="c1ccccc1",
    model=model,
    n_samples=8,
)
write_cif(results[0], "benzene_sample0.cif")

…or from the command line:

packflow predict --smiles "c1ccccc1" --model packflow-60M --n_samples 8 --out_dir generated

You can describe any molecule by its atoms and bonds (see examples/predict_arbitrary_molecule.py), or start from a structure file with packflow generate --mmcif ... / run_inference(...).

Available models: packflow-2M, packflow-20M, packflow-ddp (≡ packflow-60M), packflow-pa (GRPO). Run packflow download --list; details in packflow/checkpoints/README.md.

The packflow CLI

A single entry point covers the whole pipeline (each subcommand forwards --help):

packflow download [names...]      # fetch checkpoints from the model zoo
packflow predict   ...            # pack an arbitrary molecule (SMILES/graph) -> CIFs
packflow generate  ...            # sample crystals from a structure file -> CIFs
packflow train     ...            # train a base flow-matching model
packflow train-grpo ...           # GRPO post-training (the "PA" model)
packflow evaluate  ...            # full test-set / blind-test evaluation
packflow relax     ...            # UMA relaxation + lattice energy (Figure 5)
packflow preprocess {splits,premade} ...

End-to-end reproduction

All commands run from the repository root. The packflow CLI is the supported entry point on any machine (cluster job scripts are not part of this checkout).

1. Preprocess (CSD license) — regenerate the exact splits from the shipped refcodes in data/refcodes/; see preprocessing/README.md:

   packflow preprocess premade ...

2. Train base models (2M / 20M / 60M):

   export PACKFLOW_DATA_DIR=<your processed-data dir>
   packflow train --batch_size 128 --use_rdkit_features --use_attention_bias_from_graph --lr 3e-5

3. GRPO post-training (the PA model — a GRPO finetune of packflow-ddp):

   packflow train-grpo --model_type packflow-ddp \
       --checkpoint_path packflow/checkpoints/packflow-ddp/best_model.pt

4. Evaluate (Tables 1/2, Figure 4 λ-sweep, Figure 5 blind test):

   packflow evaluate --model packflow-pa --data_dir "$PACKFLOW_DATA_DIR"

5. Relaxation + lattice energy (Figure 5 backbone) — see packflow/relaxation/README.md:

   packflow relax --eval_dir <evaluation_results_dir> --fairchem_python "$FAIRCHEM_PYTHON"

The figure- and table-rendering code is maintained separately from this package (it depends on a large internal cache of evaluation artifacts) and is not part of the open-source surface.

External requirements

• Checkpoints (required for inference/eval). The .pt files (~1.6 GB) are kept off git and fetched from the Hugging Face Hub on first use; resolution is local-first (packflow/checkpoints/<name>/best_model.pt if present). Run packflow download to pre-fetch. See packflow/checkpoints/README.md.
• UMA weights (for relaxation / lattice energy / GRPO reward). Installed via the external/fairchem submodule; model weights download automatically on first use (cache via FAIRCHEM_CACHE_DIR).
• CSD (only to rebuild the dataset). A CCDC license + the CSD Python API (Python 3.9) are needed for preprocessing. Inference, training-from-processed-data, and evaluation do not need the CSD. No raw/processed dataset is shipped; the refcodes in data/refcodes/ let you regenerate the exact splits.

Useful environment variables

Variable	Purpose
PACKFLOW_DATA_DIR	processed-data directory used by training/eval
PACKFLOW_CHECKPOINT_DIR	where model-zoo checkpoints live
FAIRCHEM_PYTHON / FAIRCHEM_CACHE_DIR	UMA interpreter / weight cache
GENARRIS_PYTHON	python interpreter for the Genarris baseline
CSD_DATABASE_PATH	path to your local CSD database (preprocessing)
PACKFLOW_EVAL_STAGING_DIR	optional fast scratch dir for UMA staging

All paths funnel through packflow/config.py.

External code (pinned submodules)

Submodule	Commit	Use
external/Crystal_Math	8808429	CSD data extraction (our edits overlaid via preprocessing/)
external/fairchem	a0a984b7	UMA energy/relaxation + GRPO reward
external/Genarris	301a8ea	CSP baseline

Citation

If you use PackFlow, please cite the paper (arXiv:2602.20140):

@article{subramanian2026packflow,
  title={PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment},
  author={Subramanian, Akshay and Pan, Elton and Nam, Juno and Weiler, Maurice and Qu, Shuhui and Park, Cheol Woo and Jaakkola, Tommi S and Olivetti, Elsa and Gomez-Bombarelli, Rafael},
  journal={arXiv preprint arXiv:2602.20140},
  year={2026}
}

License

The source code for PackFlow is released under an MIT License (see LICENSE). However, since PackFlow was trained on data from CCDC's Cambridge Structural Database, the model weights are released under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License (see LICENSE-MODEL-WEIGHTS). For commercial use of the model weights, please ensure that you have a proper CCDC License.