# byteff2
**Repository Path**: ByteDance-Seed/byteff2
## Basic Information
- **Project Name**: byteff2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-09-15
- **Last Updated**: 2026-09-17
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
👋 Hi, everyone!
We are ByteDance Seed team.
You can get to know us better through the following channels👇
# ByteFF2
This is the source repository for ByteFF-Pol.
* [ByteFF-Pol](https://arxiv.org/abs/2508.08575) is a polarizable force field parameterized by a graph neural network (GNN), trained on high-level quantum mechanics (QM) data, thus eliminating the need for experimental calibration. ByteFF-Pol achieves exceptional accuracy in predicting the thermodynamic and transport properties of small-molecule liquids and electrolytes, outperforming SOTA traditional and ML force fields.
## News
[2025/08/25]🔥We release ByteFF-Pol.
## Getting started
### Prerequisites
* Python version >= 3.11
### Python Dependencies
All required Python packages are listed in requirements.txt. To install them, run:
```
pip install -r requirements.txt
```
### Installing Gromacs
Download Gromacs from [official website](https://manual.gromacs.org/documentation/current/download.html).
```
wget https://ftp.gromacs.org/gromacs/gromacs-2025.3.tar.gz
```
To install Gromacs, please refer to the [official documentation](https://manual.gromacs.org/documentation/current/install-guide/index.html).
```
tar xfz gromacs-2025.3.tar.gz
cd gromacs-2025.3
mkdir build
cd build
cmake .. -DGMX_BUILD_OWN_FFTW=ON -DREGRESSIONTEST_DOWNLOAD=ON
make
make check
sudo make install
source /usr/local/gromacs/bin/GMXRC
```
### Installing OpenMM for ByteFF2
To run **ByteFF2**, you need a customized version of [**OpenMM**](https://github.com/openmm/openmm) and [**OpenMM-VelocityVerlet**](https://github.com/z-gong/openmm-velocityVerlet).
1. Navigate to the `submodules/openmm` directory:
```bash
cd submodules/openmm
```
2. Run the installation script:
```bash
./install.sh [OPENMM_DIR]
```
- `[OPENMM_DIR]` (optional): Installation path for OpenMM.
- Default installation path is:
```
/usr/local/openmm
```
3. The script will:
- Compile and install the patched `openmm` (v8.3.1)
- Compile and install `openmm-velocityVerlet`
- Add required environment variables (`OPENMM_DIR` and `LD_LIBRARY_PATH`) to your `~/.bashrc`
4. After installation, restart your terminal or run:
```bash
source ~/.bashrc
```
After successful installation, you should see:
```
Success: Installed OpenMM and openmm-velocityVerlet.
```
### Trained Models
The model and configuration file are available on HuggingFace [byteff2](https://huggingface.co/ByteDance-Seed/byteff2).
To download the model and configuration file, run:
```
pip install -U "huggingface_hub[cli]"
hf download ByteDance-Seed/byteff2 --local-dir byteff2
```
## Quick Start
You can refer to several examples in the · directory; more details are available in the README.md file for each example.
* `example/1_training` contains scripts for training ByteFF-Pol.
* `example/2_compare_qm` contains scripts to compare QM and FF energies.
* `example/3_write_params` contains scripts to generate force field parameters using trained ByteFF-Pol model.
* `example/4_MD_simulations` contains scripts for molecular dynamics (MD) simulations using ByteFF-Pol.
* `example/5_similarity` contains scripts for similarity analysis using ByteFF-Pol.
## Run Tests
You can verify the environments by running the tests:
```
make test
```
## License
This project is licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).
## Citation
If you find ByteFF-Pol or ByteFF is useful for your research and applications, feel free to give us a star ⭐ or cite us using:
```bibtex
@article{zheng2026bridging,
title = {Bridging quantum mechanics to liquid properties via a universal organic force field},
author = {Tianze Zheng and Xingyuan Xu and Zhi Wang and Zhenze Yang and Yuanheng Wang and Xu Han and Lei Chen and Zhenliang Mu and Ziqing Zhang and Siyuan Liu and Sheng Gong and Kuang Yu and Wen Yan},
year = {2026},
journal = {Nature Communications},
doi = {10.1038/s41467-026-73566-3},
url = {https://www.nature.com/articles/s41467-026-73566-3}
}
@Article{D4SC06640E,
author = {Tianze Zheng and Ailun Wang and Xu Han and Yu Xia and Xingyuan Xu and Jiawei Zhan and Yu Liu and Yang Chen and Zhi Wang and Xiaojie Wu and Sheng Gong and Wen Yan},
title = {Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage},
journal = {Chem. Sci.},
year = {2025},
pages = {-},
publisher = {The Royal Society of Chemistry},
doi = {10.1039/D4SC06640E},
url = {http://dx.doi.org/10.1039/D4SC06640E}
}
```
## About [ByteDance Seed Team](https://seed.bytedance.com/)
Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.