PolyPal: A Python Package for Molecular Dynamics Simulation of Amorphous Polymers.

Area of Science:

• Polymer Science
• Materials Chemistry
• Computational Chemistry

Background:

• Porous organic polymers (POPs) are versatile materials with tunable properties.
• Molecular modeling and simulations are crucial for polymer science but underutilized for POPs.
• Existing simulation methods and force fields are not optimized for porous thermoplastic materials.

Purpose of the Study:

• To develop a streamlined workflow for all-atomistic molecular dynamics (MD) simulations of porous and nonporous polymers.
• To establish an accessible methodology for force field (FF) parametrization and polymer configuration generation.
• To enable accurate prediction of material properties for high-performance POP discovery.

Main Methods:

• Utilized ORCA, Q-Force, Assemble!, and GROMACS for FF parametrization and simulation setup.
• Developed a Python package, PolyPal, for a streamlined simulation workflow.
• Validated simulation accuracy against experimental bulk densities and fractional free volume data.

Main Results:

• The PolyPal workflow accurately reproduces experimental bulk densities and fractional free volume for amorphous polymers.
• Simulations were successfully performed on previously synthesized and characterized porous and nonporous polymers.
• Force field accuracy was confirmed through solid-state Nuclear Magnetic Resonance (NMR) studies.

Conclusions:

• The developed workflow provides a robust and accessible method for simulating amorphous polymers, including POPs.
• Accurate simulations will facilitate the rational design of novel high-performance porous organic polymers.
• This approach streamlines the simulation of previously unexplored porous polymeric materials, contributing to big data in materials science.