Open-source platform for block polymer formulation design using particle swarm optimization

Logan J. Case, Kris T. Delaney, Glenn H. Fredrickson, Frank S. Bates, Kevin D. Dorfman

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Facile exploration of large design spaces is critical to the development of new functional soft materials, including self-assembling block polymers, and computational inverse design methodologies are a promising route to initialize this task. We present here an open-source software package coupling particle swarm optimization (PSO) with an existing open-source self-consistent field theory (SCFT) software for the inverse design of self-assembling block polymers to target bulk morphologies. To lower the barrier to use of the software and facilitate exploration of novel design spaces, the underlying SCFT calculations are seeded with algorithmically generated initial fields for four typical morphologies: lamellae, network phases, cylindrical phases, and spherical phases. In addition to its utility within PSO, the initial guess tool also finds generic applicability for stand-alone SCFT calculations. The robustness of the software is demonstrated with two searches for classical phases in the conformationally symmetric diblock system, as well as one search for the Frank-Kasper [Formula: see text] phase in conformationally asymmetric diblocks. The source code for both the initial guess generation and the PSO wrapper is publicly available.

Original languageEnglish (US)
Article number115
JournalEuropean Physical Journal E
Volume44
Issue number9
DOIs
StatePublished - Sep 2021

Bibliographical note

Funding Information:
This work was supported by the DMREF Program of the National Science Foundation under awards DMR-1725272 at the University of Minnesota and DMR-1725414 at the University of California, Santa Barbara. Computational resources were provided by the Minnesota Supercomputing Institute.

Funding Information:
This work was supported by the DMREF Program of the National Science Foundation under awards DMR-1725272 at the University of Minnesota and DMR-1725414 at the University of California, Santa Barbara. Computational resources were provided by the Minnesota Supercomputing Institute.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature.

PubMed: MeSH publication types

  • Journal Article

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