Generative Modeling of Entangled Polymers with a Distance-Based Variational Autoencoder
Abstract
We present a variational autoencoder framework for learning and generating configurations of structured polymer globules from distance matrices. We used coarse-grained molecular dynamics to sample polyethylene structures, which we used as the training set for our deep learning model. By combining convolution and attention layers, the model encodes the structural patterns of distance matrices into an organized and roto-translationally invariant latent space of lower dimensionality. The generative capability of the variational autoencoder, coupled with a post-processing pipeline based on multidimensional scaling and short molecular dynamics, enables the recovery of physically meaningful configurations. The reconstructed and generated samples reproduce key observables, including energy, size, and entanglement, despite minor discrepancies in the raw decoder output.
Cite
@article{arxiv.2512.10131,
title = {Generative Modeling of Entangled Polymers with a Distance-Based Variational Autoencoder},
author = {Pietro Chiarantoni and Oscar Serra and Mohammad Erfan Mowlaei and Venkata Surya Kumar Choutipalli and Mark DelloStritto and Xinghua Shi and Micheal L. Klein and Vincenzo Carnevale},
journal= {arXiv preprint arXiv:2512.10131},
year = {2025}
}