---
license: mit
---

# SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles

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)
- [Paper on arXiv](https://arxiv.org/abs/2510.13696)

**Citation**

If you use this work, please cite:
```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}
}
```