Static structure of active fluids reflects energy dissipation

Active matter systems are driven out of equilibrium by local non-conservative forces, giving rise to unique behaviors and providing access to structures with potentially useful material properties. However, a significant obstacle for controlling the structure of active systems is their incompletely understood relationship to the dissipation of energy induced by this local driving. In this talk, I will outline our attempts to overcome this by using tools from liquid-state theories and machine learning. Our main results are a non-equilibrium mean field framework which elucidates the connection between dissipation and structure. We show how our results may be applicable to a wide range of systems, from isotropic active Ornstein-Uhlenbeck particles to more complex anisotropic active rotors, and we demonstrate that a neural network can learn this connection even without access to information about the underlying dynamics. Our results outline a new perspective on the underlying relationship between system organization and dissipation in far-from-equilibrium systems and point towards the use of similar techniques for more complex and physically relevant systems such as active nematics.