| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - chemistry |
| - biology |
| --- |
| # ByteFF2 |
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| 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)。 |
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| [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 |
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| # Trained Models |
| The `trained_models` folder contains the trained model for ByteFF-Pol and its corresponding configuration (.yaml) file. |
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| # How to use |
| Code and examples are available in the [byteff2](https://github.com/ByteDance-Seed/byteff2) repository. |
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| ## Citation |
| If you find ByteFF-Pol is useful for your research and applications, feel free to give us a star ⭐ or cite us using: |
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| ```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} |
| } |
| ``` |
