2023/07/02 by Su-Lin Wang, Yuan Chen, Wang, Sulin +5
Materials Science · Physics and Astronomy · #Block Copolymer Self-Assembly #FOS: Physical sciences #Machine Learning in Materials Science #Soft Condensed Matter (cond-mat.soft) #Theoretical and Computational Physics
paper · pdf · doi:10.48550/arxiv.2307.00659
openalex publication_date 2023/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The derivation of density functional energies from the random phase approximation of self-consistent mean field theory is generalized and applied to a binary blend of diblock copolymers and homopolymers. A nonlocal transformation is incorporated into the density functional model prior to the strong segregation extrapolation step employed by Uneyama and Doi. The transformation affords a systematic parameterization of the free energy that preserves key structural features such as scattering structure factor. A simple choice of transformation is shown to incorporate the Tuebner and Strey microemulsion structure factor and provide a reduction to the microemulsion free energy. Without adjustable parameters, the associated phase diagrams are compared to experimental and self consistent mean field based results. A gradient descent of the free energy recovers dependence of end-state morphology on initial configurations, and identifies coexisting microstructures and transitions to two-phase behavior. Small angle x-ray data is simulated and used in classification of microphase morphology.