Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries

Three-dimensional atomic-level models of polymers are the starting pointsfor physics-based simulation studies. A capability to generate reasonable initial structuralmodels is highly desired for this purpose. We have developed a python toolkit, namely,polymer structure predictor (PSP), to generate a hierarchy of polymer models, ranging fromoligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input.This toolkit allows users to tune several parameters to manage the quality and scale ofmodels and computational cost. The output structures and accompanying forcefield(GAFF2/OPLS-AA) parameterfiles can be used for downstreamab initioand moleculardynamics simulations. ThePSPpackage includes a Colab notebook where users can gothrough several examples, building their own models, visualizing them, and downloadingthem for later use. ThePSPtoolkit, being afirst of its kind, will facilitate automation inpolymer property prediction and design.

Automated summary

Researchers have developed a Python toolkit called PSP for generating polymer models based on SMILES strings. Users can adjust parameters to manage the quality and scale of models, with output structures and forcefield parameter files available for downstream simulations. The PSP package also includes a Colab notebook for user interaction and learning.