Streamline README

README.md

@@ -2,14 +2,9 @@
 license: mit
 ---
 
-# SimPoly
+# SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles
 
-SimPoly is a fast and scalable machine learning force field (MLFF) for ab initio prediction of polymer properties. This repository contains the trained model weights and benchmark datasets from our paper. Refer to the accompanying GitHub repository for instructions and usage examples.
-
-**Key Features**
-- Accurately predicts polymer densities and glass transition temperatures without experimental fitting
-- Outperforms classical force fields for a broad range of polymers
-- Includes benchmark data: experimental bulk properties for 130 polymers and quantum-chemical training datasets
+SimPoly is a fast and scalable machine-learned force field (MLFF) for polymer systems. This repository contains the trained model weights and training datasets from our [paper](https://arxiv.org/abs/2510.13696). Refer to the accompanying GitHub repository for instructions and usage examples.
 
 **Resources**
 - [GitHub Code](https://github.com/microsoft/simpoly)
@@ -18,14 +13,16 @@ SimPoly is a fast and scalable machine learning force field (MLFF) for ab initio
 **Citation**
 
 If you use this work, please cite:
-```
-@misc{simm2025simpolysimulationpolymersmachine,
-      title={SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles}, 
-      author={Gregor N. C. Simm and Jean Hélie and Hannes Schulz and Yicheng Chen and Guillem Simeon and Anna Kuzina and Ernesto Martinez-Baez and Piero Gasparotto and Gabriele Tocci and Chi Chen and Yatao Li and Lixue Cheng and Zun Wang and Bichlien H. Nguyen and Jake A. Smith and Lixin Sun},
-      year={2025},
-      eprint={2510.13696},
-      archivePrefix={arXiv},
-      primaryClass={physics.chem-ph},
-      url={https://arxiv.org/abs/2510.13696}, 
+```bibtex
+@misc{Simm2025SimPoly,
+  title = {SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles},
+  author = {Simm, Gregor N. C. and H{\'e}lie, Jean and Schulz, Hannes and Chen, Yicheng and Simeon, Guillem and Kuzina, Anna and {Martinez-Baez}, Ernesto and Gasparotto, Piero and Tocci, Gabriele and Chen, Chi and Li, Yatao and Cheng, Lixue and Wang, Zun and Nguyen, Bichlien H. and Smith, Jake A. and Sun, Lixin},
+  year = 2025,
+  number = {arXiv:2510.13696},
+  eprint = {2510.13696},
+  primaryclass = {physics},
+  publisher = {arXiv},
+  doi = {10.48550/arXiv.2510.13696},
+  archiveprefix = {arXiv}
 }
-```
+```