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。
ByteFF-Pol 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 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:
@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}
}