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Harikrishna Sahu1, Kuan-Hsuan Shen1, Joseph H Montoya2

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Summary
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A new Python toolkit, polymer structure predictor (psp), generates atomic-level polymer models from SMILES strings. This tool aids in polymer property prediction and design by creating diverse polymer structures for simulations.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Polymer Science

Background:

  • Accurate three-dimensional atomic-level polymer models are crucial for physics-based simulations.
  • Generating reliable initial structural models is a significant challenge in polymer science.

Purpose of the Study:

  • To develop a user-friendly Python toolkit for generating diverse polymer structural models.
  • To facilitate the automation of polymer property prediction and design.

Main Methods:

  • Developed the polymer structure predictor (psp) Python toolkit.
  • Utilized simplified molecular-input line-entry system (SMILES) strings as primary input.
  • Generated models ranging from oligomers to amorphous structures with tunable parameters.

Main Results:

  • The psp toolkit generates polymer models from oligomers to amorphous structures.
  • Output includes structures and force field parameters (GAFF2/OPLS-AA) for simulations.
  • An integrated Colab notebook allows for model building, visualization, and downloading.

Conclusions:

  • The psp toolkit provides a novel capability for creating polymer models.
  • It enables downstream ab initio and molecular dynamics simulations.
  • Facilitates automated polymer property prediction and materials design.