Mutual Information Reveals Emergent Effects of Self-Avoidant Memory in Curvature Statistics of Particle Paths.

ORAL

Abstract

Chemically induced swimming of microdroplets motivates a model for self-avoidant particles which exhibit novel trajectory statistics at long timescales. As the particles stochastically explore the self-generated and time-evolving concentration field, they often turn in on their own past history and create concentration "traps" which arrest the overall displacement for a period of time. This transient self-trapping is revealed in the path data as areas of extremely high correlated curvature and it is expressed statistically as suppressed enhanced diffusion when compared to traditional active particle models. We explore the shortcomings of traditional models in explaining this behavior and suggest mutual information as an additional tool can be used to quantify this emergent effect.

*This research was supported in part by an appointment with the National Science Foundation (NSF) Mathematical Sciences Graduate Internship (MSGI) Program sponsored by the NSF Division of Mathematical Sciences. This program is administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and NSF. ORISE is managed for DOE by ORAU. All opinions expressed in this paper are the author's and do not necessarily reflect the policies and views of NSF, ORAU/ORISE, or DOE.

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Publication: Self-avoidant memory effects on enhanced diffusion in a stochastic model of environmentally responsive swimming droplets:
Katherine Daftari and Katherine A. Newhall
Phys. Rev. E 105, 024609 – Published 23 February 2022

Presenters

  • Katherine Daftari

    • University of North Carolina at Chapel Hill

Authors

  • Katherine Daftari

    • University of North Carolina at Chapel Hill