Low rattling: a predictive principle for self-organization in active collectives Pavel Chvykov,1 Thomas A. Berrueta,2 Akash Vardhan,3 William Savoie,3 Alexander Samland,2 Todd D. Murphey,2 3 3 3;4 Kurt Wiesenfeld, Daniel I. Goldman, Jeremy L. England ∗ 1Physics of Living Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA 2Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, USA 3School of Physics, Georgia Institute of Technology, Atlanta, Georgia 30332, USA 4GlaxoSmithKline AI/ML, 200 Cambridgepark Drive, Cambridge, Massachusetts 02140, USA ∗To whom correspondence should be addressed; E-mail:
[email protected] Self-organization is frequently observed in active collectives, from ant rafts to molecular motor assemblies. General principles describing self-organization away from equilibrium have been challenging to iden- tify. We offer a unifying framework that models the behavior of com- plex systems as largely random, while capturing their configuration- dependent response to external forcing. This allows derivation of a Boltzmann-like principle for understanding and manipulating driven self-organization. We validate our predictions experimentally in shape- changing robotic active matter, and outline a methodology for con- trolling collective behavior. Our findings highlight how emergent or- arXiv:2101.00683v1 [cond-mat.stat-mech] 3 Jan 2021 der depends sensitively on the matching between external patterns of forcing and internal dynamical response properties, pointing towards future approaches for design and control of active particle mixtures and metamaterials. 1 ACCEPTED VERSION: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science, (2021-01-01), doi: 10.1126/science.abc6182 Self-organization in nature is surprising because getting a large group of separate particles to act in an organized way is often difficult.