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

H Sahu and KH Shen and JH Montoya and H Tran and R Ramprasad, JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 18, 2737-2748 (2022).

DOI: 10.1021/acs.jctc.2c00022

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.

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