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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}
}
```
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