Polyply: a python suite for facilitating simulations of (bio-)macromolecules and nanomaterials
Abstract
Molecular dynamics simulations play an increasingly important role in the rational design of (nano)-materials and in the study of biomacromolecules. However, generating input files and realistic starting coordinates for these simulations is a major bottleneck, especially for high throughput protocols and for complex multi-component systems. To eliminate this bottleneck, we present the polyply software suite that leverages 1) a multi-scale graph matching algorithm designed to generate parameters quickly and for arbitrarily complex polymeric topologies, and 2) a generic multi-scale random walk protocol capable of setting up complex systems efficiently and independent of the target force-field or model resolution. We benchmark quality and performance of the approach by creating melt simulations of six different polymers using two force-fields with different resolution. We further demonstrate the power of our approach by setting up a multi lamellar microphase-separated block copolymer system for next generation batteries, and by generating a liquid-liquid phase separated polyethylene oxide-dextran system inside a lipid vesicle, featuring both branching and molecular weight distribution of the dextran component.
Keywords
Cite
@article{arxiv.2105.05890,
title = {Polyply: a python suite for facilitating simulations of (bio-)macromolecules and nanomaterials},
author = {Fabian Grünewald and Riccardo Alessandri and Peter C. Kroon and Luca Monticelli and Paulo C. T. Souza and Siewert J. Marrink},
journal= {arXiv preprint arXiv:2105.05890},
year = {2022}
}