update readme

README.md

@@ -1,33 +1,33 @@
----
-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}
-}
+---
+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}
+}
 ```