GenSI/CrysBFN

[ICLR 2025 Spotlight] A Periodic Bayesian Flow for Material Generation (CrysBFN)

[2025/07] We upload the pretrained checkpoints here with instructions below.

This is the implementation code for ICLR 2025 Spotlight paper CrysBFN.

[paper] [website]

Install

1. Set up environment variables

Firstly please set up dot environment variables in .env file.

• PROJECT_ROOT: path to the folder that contains this repo. e.g. /data/wuhl/CrysBFN
• HYDRA_JOBS: path to a folder to store hydra outputs. This is the directory where we store checkpoints. e.g. /data/wuhl/CrysBFN/hydra
• WABDB: path to a folder to store wandb outputs e.g. /data/wuhl/CrysBFN/wandb

2. Install with Mamba

We recommend using Mamba or conda (with libmamba solver) to build the python environment. It may take several minutes to solve the environment—please wait patiently.

conda env create -f environment.yml
conda activate crysbfn

Training, Sampling and Evaluation

We use shell scripts in scripts to manage all pipelines. Hyper-parameters can be set in those shell script files. Scripts to launch experiments can be found in scripts/csp_scripts and scripts/gen_scripts for crystal structure prediction task and de novo generation task.

Training

For launching a de novo generation task training experiment, please use the following code:

bash ./scripts/gen_scripts/mp20_exps.sh

Every first run on each dataset requires longer time (< 1 hour) for preparing the cache processed data. For launching a crystal structure prediction task training experiment, please use the following code:

bash ./scripts/csp_scripts/mp20_exps.sh

Sampling and Evaluating

After training, please modify the MODEL_PATH variable as the hydra directory of the training experiment. Then, use the below code to generate and evaluating samples.

bash scripts/csp_scripts/eval_mp20.sh

Use Our Checkpoints

We provide our checkpoints here. Here is a fastest example (NFE=10) to use the checkpoint to verify your installation:

1. Download the zip file into the hydra directory and unzip it

cd hydra
unzip ./mp20_csp_s10.zip

2. Modify the first line in scripts/csp_scripts/mp20_eval.sh

MODEL_PATH=/data/wuhl/CrysBFN/hydra/mp20_csp_s10 # modify according to your path

3. Run the code to sample and eval

cd ..
bash scripts/csp_scripts/mp20_eval.sh

Toy Example

We provide toy examples with minimal components illustrating how BFNs work in ./toy_example.

Citation

If you find this repo or our paper useful, please cite our paper :)

@misc{wu2025periodicbayesianflowmaterial,
        title={A Periodic Bayesian Flow for Material Generation}, 
        author={Hanlin Wu and Yuxuan Song and Jingjing Gong and Ziyao Cao and Yawen Ouyang and Jianbing Zhang and Hao Zhou and Wei-Ying Ma and Jingjing Liu},
        year={2025},
        eprint={2502.02016},
        archivePrefix={arXiv},
        primaryClass={cs.LG},
        url={https://arxiv.org/abs/2502.02016}, 
  }

Acknowledgement

The main structure of this repository is mainly based on CDVAE. The environment configuration file is modified after environment.yml in FlowMM.