| | ---
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| | license: apache-2.0
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| | tags:
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| | - chemistry
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| | - biology
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| | ---
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| | # ByteFF2
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| |
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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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| |
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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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| |
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| | # Trained Models
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| | The `trained_models`` folder contains the trained model for ByteFF-Pol and its corresponding configuration (.yaml) file.
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| |
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| | # How to use
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| | Code and examples are available in the [byteff2](https://github.com/ByteDance-Seed/byteff2) repository.
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| |
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| | ## Citation
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| | 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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| |
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| | ```bibtex
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| |
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| | @misc{zheng2025bridgingquantummechanicsorganic,
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| | title = {Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field},
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| | 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},
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| | year = {2025},
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| | eprint = {2508.08575},
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| | archivePrefix = {arXiv},
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| | primaryClass = {physics.comp-ph},
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| | url = {https://arxiv.org/abs/2508.08575}
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| | }
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| | ``` |
