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We use neuroevolutionary learning to identify time-dependent protocols forlow-dissipation self-assembly in a model of generic active particles withinteractions. When the time allotted for assembly is sufficiently long,low-dissipation protocols use only interparticle attractions, producing anamount of entropy that scales as the number of particles. When time is tooshort to allow assembly to proceed via diffusive motion, low-dissipationassembly protocols instead require particle self-propulsion, producing anamount of entropy that scales with the number of particles and the swim lengthrequired to cause assembly. Self-propulsion therefore provides an expensive butnecessary mechanism for inducing assembly when time is of the essence.

Low-dissipation self-assembly protocols of active sticky particles