An extensible density-biasing approach for molecular simulations of multicomponent block copolymers

Area of Science:

• Polymer Science
• Computational Chemistry
• Materials Science

Background:

• Generating accurate molecular structures for complex block copolymers is crucial for predicting material properties.
• Existing methods may struggle with the intricate architectures of advanced polymer systems.

Purpose of the Study:

• To introduce a new node-density biased Monte Carlo methodology for generating molecular structures of complex block copolymers.
• To enhance polymer structure generation by incorporating global structural considerations into the sampling process.

Main Methods:

• Representing block copolymers as bead-spring models.
• Utilizing self-consistent field theory to calculate monomer density fields.
• Employing a biased random walk configuration generation, modified to consider global polymer structure.

Main Results:

• The methodology successfully generates molecular configurations by controlling random walks based on density field differences.
• Demonstrated viability through molecular dynamics simulations.
• Successfully generated configurations for challenging systems like ABABA pentablock copolymers and ABC triblock terpolymers.

Conclusions:

• The proposed node-density biased Monte Carlo method offers a powerful approach for generating diverse and accurate block copolymer structures.
• This enhanced methodology expands the range of materials for which molecular configurations can be reliably generated.