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

Area of Science:

• Soft Materials Science
• Computational Polymer Physics
• Materials Informatics

Background:

• Exploring large design spaces is crucial for developing new functional soft materials like self-assembling block polymers.
• Computational inverse design methodologies offer a promising approach to initialize this exploration.
• Existing tools often have high barriers to entry, limiting design space exploration.

Purpose of the Study:

• To present an open-source software package for the inverse design of self-assembling block polymers.
• To couple particle swarm optimization (PSO) with self-consistent field theory (SCFT) for targeting bulk morphologies.
• To lower the barrier to entry for exploring novel material design spaces.

Main Methods:

• Developed an open-source software package integrating PSO with an existing SCFT code.
• Implemented an initial guess tool using algorithmically generated fields for four typical morphologies (lamellae, network, cylindrical, spherical).
• Validated the software's robustness through searches for classical phases in symmetric diblocks and the Frank-Kasper phase in asymmetric diblocks.

Main Results:

• Successfully coupled PSO with SCFT for inverse polymer design.
• The initial guess tool demonstrated generic applicability for stand-alone SCFT calculations.
• The software efficiently identified target bulk morphologies, including complex phases like the Frank-Kasper phase.

Conclusions:

• The developed open-source software facilitates the exploration of large design spaces for self-assembling block polymers.
• The initial guess tool significantly lowers the barrier to entry for SCFT calculations.
• Public availability of the source code promotes wider adoption and further development in soft materials design.