Typically, 3D atomic models are required for physics-based simulations of materials. Within the specific area of polymer science, an automatic engine for generating such polymer models is needed. We have developed a python toolkit named Polymer Structure Predictor (PSP) for suggesting a hierarchy of polymer models, ranging from oligomer/infinite polymer chains to sophisticated amorphous models, which can be used downstream in physics-based simulations. The only input of PSP is the simplified molecular-input line-entry system (SMILES) strings of the repeat unit of the polymer. An overview of the PSP workflow is shown in Figure 1. The performance of PSP was tested by comparing the generated models with the known experimental data of several polymers. The output files can be directly used with several computational software, such as VASP, ORCA, LAMPPS, and GAMESS, allowing automation for computing properties. PSP has already been used in the polymer version of computational autonomy for materials discovery (CAMD), establishing extensive databases for polymer bandgap and charge injection barriers that power the Polymer Genome platform (www.polymergenome.org). PSP is expected to benefit a wide number of academic and industrial research activities, by accelerating polymer discovery.

PSP: A toolkit for the efficient generation of 3D atomic-level polymer models

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.