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. Refer to the accompanying GitHub repository for instructions and usage examples.
Resources
Citation
If you use this work, please cite:
@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}
}