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Low-dissipation self-assembly protocols of active sticky particles.

Stephen Whitelam1, Jeremy D Schmit2

  • 1Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA.

Journal of Crystal Growth
|March 27, 2023
PubMed
Summary
This summary is machine-generated.

Neuroevolutionary learning optimizes low-dissipation self-assembly protocols for active particles. Fast assembly requires costly self-propulsion, while slow assembly relies on particle attractions, impacting entropy production.

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Area of Science:

  • Physics, Soft Matter
  • Computational Science
  • Biophysics

Background:

  • Self-assembly is crucial for creating complex structures from simple components.
  • Controlling active matter self-assembly with minimal energy dissipation is a significant challenge.
  • Understanding the role of time and particle dynamics in assembly is essential.

Purpose of the Study:

  • To identify time-dependent protocols for low-dissipation self-assembly in active matter.
  • To investigate the trade-offs between assembly speed, energy cost, and entropy production.
  • To explore the necessity of particle self-propulsion for rapid assembly.

Main Methods:

  • Utilizing neuroevolutionary learning to discover optimal assembly protocols.
  • Modeling generic active particles with interaction potentials.
  • Analyzing entropy production as a function of assembly time and particle behavior.

Main Results:

  • Low-dissipation protocols depend on assembly time.
  • Sufficient time allows assembly via interparticle attractions, with entropy scaling linearly with particle number.
  • Short assembly times necessitate particle self-propulsion, increasing entropy production proportionally to particle number and swim length.

Conclusions:

  • Self-propulsion is an energetically expensive but vital mechanism for achieving rapid self-assembly in active matter.
  • The choice between attractive forces and self-propulsion for assembly is dictated by the available time.
  • Neuroevolution offers a powerful approach to designing efficient self-assembly strategies.