Entropy production fluctuations encode collective behavior in active matter

GrandPre, Trevor; Klymko, Katherine; Mandadapu, Kranthi K.; Limmer, David T.

doi:10.1103/physreve.103.012613

Cited by 48 publications

(47 citation statements)

References 93 publications

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“…Homogeneous nucleation and stability.-Despite recent progress in the development of importance sampling techniques for nonequilibrium systems [64][65][66][67][68][69][70][71][72][73], the ability to comprehensively survey the phase behavior of many-particle active systems [74][75][76][77] remains limited. In the absence of these tools, we make an appeal to two-state rate theory to identify the relative stability of the two coexistence scenarios.…”

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confidence: 99%

Phase Diagram of Active Brownian Spheres: Crystallization and the Metastability of Motility-Induced Phase Separation

et al. 2021

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Motility-induced phase separation (MIPS), the phenomenon in which purely repulsive active particles undergo a liquid-gas phase separation, is among the simplest and most widely studied examples of a nonequilibrium phase transition. Here, we show that states of MIPS coexistence are in fact only metastable for three-dimensional active Brownian particles over a very broad range of conditions, decaying at long times through an ordering transition we call active crystallization. At an activity just above the MIPS critical point, the liquid-gas binodal is superseded by the crystal-fluid coexistence curve, with solid, liquid, and gas all coexisting at the triple point where the two curves intersect. Nucleating an active crystal from a disordered fluid, however, requires a rare fluctuation that exhibits the nearly close-packed density of the solid phase. The corresponding barrier to crystallization is surmountable on a feasible timescale only at high activity, and only at fluid densities near maximal packing. The glassiness expected for such dense liquids at equilibrium is strongly mitigated by active forces, so that the lifetime of liquid-gas coexistence declines steadily with increasing activity, manifesting in simulations as a facile spontaneous crystallization at extremely high activity.

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Phase Diagram of Active Brownian Spheres: Crystallization and the Metastability of Motility-Induced Phase Separation

et al. 2021

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“…A general approach to the optimization of a sampling dynamics based on a variational principle for the Doob transform for diffusive processes has recently been developed. 25 Within this context of diffusive processes, optimal forces have been used to elucidate mechanisms and rates of nonlinear response, 26,27 to encode dynamical phase diagrams, [28][29][30] and to deduce inverse design principles. 31,32 In this work we aim to extend a reinforcement learning 11 based approach to the optimization of a sampling dynamics to diffusive systems, building on the work of Refs.…”

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confidence: 99%

Reinforcement learning of rare diffusive dynamics

Das

Rose

Garrahan

et al. 2021

The Journal of Chemical Physics

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We present a method to probe rare molecular dynamics trajectories directly using reinforcement learning. We consider trajectories that are conditioned to transition between regions of configuration space in finite time, like those relevant in the study of reactive events, as well as trajectories exhibiting rare fluctuations of time-integrated quantities in the long time limit, like those relevant in the calculation of large deviation functions. In both cases, reinforcement learning techniques are used to optimize an added force that minimizes the Kullback-Leibler divergence between the conditioned trajectory ensemble and a driven one. Under the optimized added force, the system evolves the rare fluctuation as a typical one, affording a variational estimate of its likelihood in the original trajectory ensemble. Low variance gradients employing value functions are proposed to increase the convergence of the optimal force. The method we develop employing these gradients leads to efficient and accurate estimates of both the optimal force and the likelihood of the rare event for a variety of model systems.

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“…Another example is the so-called speed-limit inequalities [24][25][26][27][28][29][30][31], which indicate a trade-off between efficiency and power output for general Markov processes. Most recently, Dechant and Sasa [32] derived an upper bound for non-equilibrium response function in terms of fluctuations and relative entropy between the perturbed and reference states, which generated immediate interests [21,33,34]. Given the large number of works published in recent years, it is highly desirable and urgent to understand whether these inequalities are consequences of more fundamental aspects of non-equilibrium physics, such as Fluctuation Theorems and Markovian property, or rather depend on specific system details.…”

Section: Introductionmentioning

confidence: 99%

Absence of Thermodynamic Uncertainty Relations with Asymmetric Dynamic Protocols

Ding

Huang

Xing

2021

Preprint

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Many versions of Thermodynamic Uncertainty Relations (TUR) have recently been discovered, which impose lower bounds on relative fluctuations of integrated currents in irreversible dissipative processes, and suggest that there may be fundamental limitations on the precision of small scale machines and heat engines. In this work we rigorously demonstrate that TUR can be evaded by using dynamic protocols that are asymmetric under time-reversal. We illustrate our results using a model heat engine using two-level systems, and also discuss heuristically the fundamental connections between TUR and time-reversal symmetry.

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Entropy production fluctuations encode collective behavior in active matter

Cited by 48 publications

References 93 publications

Phase Diagram of Active Brownian Spheres: Crystallization and the Metastability of Motility-Induced Phase Separation

Phase Diagram of Active Brownian Spheres: Crystallization and the Metastability of Motility-Induced Phase Separation

Reinforcement learning of rare diffusive dynamics

Absence of Thermodynamic Uncertainty Relations with Asymmetric Dynamic Protocols

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