| ---
|
| 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}
|
| }
|
| ```
|
|
|
