Stochastic Density Functional Theory on Lane Formation in Electric-Field-Driven Ionic Mixtures: Flow-Kernel-Based Formulation
Hiroshi Frusawa

TL;DR
This paper develops a stochastic density functional theory with multiplicative noise to analyze lane formation in electric-field-driven ionic mixtures, linking linear stability analysis with charge correlation functions and domain pattern formation.
Contribution
It extends deterministic dynamical DFT by incorporating stochastic effects, providing a new framework to understand non-equilibrium lane formation in ionic mixtures.
Findings
Derived the stochastic DFT with multiplicative noise for laning phenomena.
Linked linear stability analysis to charge-charge correlation functions.
Demonstrated stripe domain formation through correlation functions and inverse Fourier transforms.
Abstract
Simulation and experimental studies have demonstrated non-equilibrium ordering in driven colloidal suspensions: with increasing driving force, a uniform colloidal mixture transforms into a locally demixed state characterized by the lane formation or the emergence of strongly anisotropic stripe-like domains. Theoretically, we have found that a linear stability analysis of density dynamics can explain the non-equilibrium ordering by adding a non-trivial advection term. This advection arises from fluctuating flows due to non-Coulombic interactions associated with oppositely driven migrations. Recent studies based on the dynamical density functional theory (DFT) without multiplicative noise have introduced the flow kernel for providing a general description of the fluctuating velocity. Here, we assess and extend the above deterministic DFT by treating electric-field-driven binary ionic…
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