Near-optimal protocols in complex nonequilibrium transformations

By Todd R. Gingrich, Grant M. Rotskoff, Gavin E. Crooks, P. Geissler. 2016

53 citations recorded by the source corpus.

Significance Classical thermodynamics was developed to help design the best protocols for operating heat engines that remain close to equilibrium at all times. Modern experimental techniques for manipulating microscopic and mesoscopic systems routinely access far-from-equilibrium states, demanding new theoretical tools to describe the optimal protocols in this more complicated regime. Prior studies have sought, in simple models, the protocol that minimizes dissipation. We use computational tools to investigate the diversity of low-dissipation protocols. We show that optimal protocols can be accompanied by a vast set of near-optimal protocols, which still offer the substantive benefits of the optimal protocol. Although solving for the optimal protocol is typically difficult, computationally identifying a near-optimal protocol can be comparatively easy. The development of sophisticated experimental means to control nanoscale systems has motivated efforts to design driving protocols that minimize the energy dissipated to the environment. Computational models are a crucial tool in this practical challenge. We describe a general method for sampling an ensemble of finite-time, nonequilibrium protocols biased toward a low average dissipation. We show that this scheme can be carried out very efficiently in several limiting cases. As an application, we sample the ensemble of low-dissipation protocols that invert the magnetization of a 2D Ising model and explore how the diversity of the protocols varies in response to constraints on the average dissipation. In this example, we find that there is a large set of protocols with average dissipation close to the optimal value, which we argue is a general phenomenon.

Subfields: Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics, AI for Physical Sciences

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Citations in the corpus

Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net (2017, methodology): “For example, physical methods for ﬁnding optimal thermodynamic paths that minimize heat dissipation (Schmiedl and Seifert, 2007; Sivak and Crooks, 2012; Gingrich et al., 2016), could potentially be exploited to tighten lowerbounds in variational inference.”
Reinforcement Learning in Different Phases of Quantum Control (2017, methodology): “Using RL, we identify nearly optimal control protocols [58] which can be parametrized by a few d.”