Swarm behavior is a nonequilibrium phenomenon that takes place in a wide range of biological and natural systems. Swarming involves the emergence of intelligent collective behavior and leads to highly energy-efficient assemblies. Here, we combine computational and experimental methods to unravel and control self-organization and swarming in active matter systems of light-activated and shape-changing colloidal particles. Using a multi-scale simulation framework to model the assembly process in active matter and recent advances in the determination of nonequilibrium entropies, the computational analysis sheds light on the intelligence-information link in active assemblies. This is achieved through the calculation of an entropy characteristic of the configuration of the assembly (degree of organization) through machine learning and pattern recognition, and of an entropy measuring the information contained in the assembly (bits or shannons) through a Kolmogorov complexity approach. The assembly rules identified here will provide the blueprint for a bottom up approach to micro- and nanorobotics, with active particles swarming and forming large assemblies to perform macroscopic tasks.

Self-organization and swarm behavior in active matter: A combined computational-experimental analysis of the intelligence-information interplay

ACS Spring Meeting 2021

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