MPolSys Modeler, a tool for computational modeling of linear polymer systems

Filipe Morais Frade De Faria,Reginaldo Gonçalves Leão Junior

doi:10.33448/rsd-v10i16.24007

Abstract

The computational study of intermolecular relationships of a given material can be used as a route for predicting quantities impossible or difficult to be determined experimentally. Furthermore properties of new materials can also be predicted by techniques of this type, when they are still in the modeling phase. This technique reproduces the classical dynamic relationships between the constituent elements of the material, atoms or unicorpuscular approximations of molecules, from interaction potential models called force fields. This work aims to develop a tool that performs the composition of linear polymeric chain systems through a self-avoided walk. For this, the concept of self-experimentation of long walks (SAWLC) was used, together with the Python language to develop MpolSys Modeler. This tool is a non-overlapping polymer chain generator, which in turn generates outputs that can be used as input to Moltemplate. To validate the tool's results, experiments were carried out in which the numbers and polymerization chains of the simulated polymer were varied, observing the overlap or not of the molecules that make up the simulation. At the end of the simulations, there were positive results that indicate a promising usage of the tool for the creation of polymers with a high number of chains and degrees of polymerization.

Highlights

Developing computational molecular dynamics (CMD) techniques is an arduous task that is associated with many possibilities of error during the modeling process
The input composition of multipurpose simulation systems such as Largescale Atomic/Molecular Massively Parallel Simulator (LAMMPS), a classical molecular dynamics code with a focus on materials modeling developed by Plimpton et al (2020), is a task that is almost impossible to accomplished manually
As in all other fields of science, but especially in molecular dynamics, the reproducibility of experiments is the only way to guarantee the reliability of the method, data and the assertions resulting from successful research

Summary

Introduction

Developing computational molecular dynamics (CMD) techniques is an arduous task that is associated with many possibilities of error during the modeling process. This is due to the need to determine the spatial arrangement of the model elements in the initial condition, correct description of the connections between particles and accurate selection of the reference simulation potentials (force fields). Gartner III and Jayaraman (2019), reviewed modeling and simulating polymers by CMD studies. They pointeds out results from a wrong model, or a poorly parameterized simulation, do not appear to be obvious errors and can, even in these cases be understood as acceptable

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Results