From predicting to learning dissipation from pair correlations of active liquids

TL;DR

This paper develops a theoretical and machine learning framework to connect the static structure of active liquids with their energy dissipation, enabling better understanding and prediction of nonequilibrium behaviors.

Contribution

It introduces a novel mean-field theory for predicting dissipation from pair correlations in strongly interacting active systems, and demonstrates a neural network approach for direct dissipation prediction from configurations.

Findings

Dissipation closely relates to pair correlations under active temperature conditions.

The mean-field framework accurately predicts dissipation even far from equilibrium.

Neural networks can map static configurations to dissipation rates without prior dynamics knowledge.

Abstract

Active systems, which are driven out of equilibrium by local non-conservative forces, can adopt unique behaviors and configurations. An important challenge in the design of novel materials which utilize such properties is to precisely connect the static structure of active systems to the dissipation of energy induced by the local driving. Here, we use tools from liquid-state theories and machine learning to take on this challenge. We first demonstrate analytically for an isotropic active matter system that dissipation and pair correlations are closely related when driving forces behave like an active temperature. We then extend a nonequilibrium mean-field framework for predicting these pair correlations, which unlike most existing approaches is applicable even for strongly interacting particles and far from equilibrium, to predicting dissipation in these systems. Based on this theory, we reveal a robust analytic relation between dissipation and structure which holds even as the system approaches a nonequilibrium phase transition. Finally, we construct a neural network which maps static configurations of particles to their dissipation rate without any prior knowledge of the underlying dynamics. Our results open novel perspectives on the interplay between dissipation and organization out-of-equilibrium.