|
|
--- |
|
|
license: apache-2.0 |
|
|
tags: |
|
|
- chemistry |
|
|
- biology |
|
|
--- |
|
|
# ByteFF2 |
|
|
|
|
|
This repository contains the model used for the paper [Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field](https://arxiv.org/abs/2508.08575)。 |
|
|
|
|
|
[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 |
|
|
|
|
|
# Trained Models |
|
|
The `trained_models` folder contains the trained model for ByteFF-Pol and its corresponding configuration (.yaml) file. |
|
|
|
|
|
# How to use |
|
|
Code and examples are available in the [byteff2](https://github.com/ByteDance-Seed/byteff2) repository. |
|
|
|
|
|
## Citation |
|
|
If you find ByteFF-Pol is useful for your research and applications, feel free to give us a star ⭐ or cite us using: |
|
|
|
|
|
```bibtex |
|
|
|
|
|
@misc{zheng2025bridgingquantummechanicsorganic, |
|
|
title = {Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field}, |
|
|
author = {Tianze Zheng and Xingyuan Xu and Zhi Wang and Xu Han and Zhenliang Mu and Ziqing Zhang and Sheng Gong and Kuang Yu and Wen Yan}, |
|
|
year = {2025}, |
|
|
eprint = {2508.08575}, |
|
|
archivePrefix = {arXiv}, |
|
|
primaryClass = {physics.comp-ph}, |
|
|
url = {https://arxiv.org/abs/2508.08575} |
|
|
} |
|
|
``` |
