File size: 3,626 Bytes
bde6d0a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | ## [ICLR 2025 Spotlight] A Periodic Bayesian Flow for Material Generation (CrysBFN)
> [!IMPORTANT]
> [2025/07] We upload the pretrained checkpoints [here](https://drive.google.com/drive/folders/1W5kGiZYFRJZiyKyTwCdcPk9lbjTsTCO-?usp=drive_link) with instructions below.
This is the implementation code for ICLR 2025 Spotlight paper CrysBFN.
[\[paper\]](arxiv.org/pdf/2502.02016)
[\[website\]](https://t.co/a4x4qlROH7)
## 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](https://github.com/conda-forge/miniforge) 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](https://drive.google.com/drive/folders/1W5kGiZYFRJZiyKyTwCdcPk9lbjTsTCO-?usp=drive_link). 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](https://github.com/txie-93/cdvae). The environment configuration file is modified after environment.yml in [FlowMM](https://github.com/txie-93/cdvae).
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