Small Volume Fraction Limit of the Diblock Copolymer Problem: I. Sharp Interface Functional
R. Choksi, M. A. Peletier

TL;DR
This paper analyzes the small volume fraction limit of a sharp-interface nonlocal functional modeling diblock copolymer microphase separation, deriving effective energies that describe particle concentration and interactions.
Contribution
It introduces a novel Gamma-convergence analysis of the sharp-interface functional in the small volume fraction limit, revealing effective energies with local and Coulomb-like interaction terms.
Findings
Effective energies are finite only on point particles.
The highest level energy is local, capturing individual particle structure.
The next level includes Coulomb-like interactions responsible for pattern formation.
Abstract
We present the first of two articles on the small volume fraction limit of a nonlocal Cahn-Hilliard functional introduced to model microphase separation of diblock copolymers. Here we focus attention on the sharp-interface version of the functional and consider a limit in which the volume fraction tends to zero but the number of minority phases (called particles) remains O(1). Using the language of Gamma-convergence, we focus on two levels of this convergence, and derive first and second order effective energies, whose energy landscapes are simpler and more transparent. These limiting energies are only finite on weighted sums of delta functions, corresponding to the concentration of mass into `point particles'. At the highest level, the effective energy is entirely local and contains information about the structure of each particle but no information about their spatial distribution. At…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBlock Copolymer Self-Assembly · Theoretical and Computational Physics · Stochastic processes and statistical mechanics
