David A. Sivak, John D. Chodera, Gavin E. Crooks, Phys. Rev. X 3, 011007 (2013)

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Abstract: Common algorithms for computationally simulating Langevin dynamics must discretize the stochastic differential equations of motion. These resulting finite-time-step integrators necessarily have several practical issues in common: Microscopic reversibility is violated, the sampled stationary distribution differs from the desired equilibrium distribution, and the work accumulated in nonequilibrium simulations is not directly usable in estimators based on nonequilibrium work theorems. Here, we show that, even with a time-independent Hamiltonian, finite-time-step Langevin integrators can be thought of as a driven, nonequilibrium physical process. Once an appropriate worklike quantity is defined – here called the shadow work – Continue reading →

David A. Sivak, John D. Chodera, Gavin E. Crooks, arXiv:1301.3800

[ Full text | ArXiv ]

Abstract: While the numerical integration of deterministic equations of motion for molecular systems now has a well-developed set of algorithms with commonly agreed-upon desirable properties, the simulation of stochastic equations of motion lacks algorithms with a similar degree of universal acceptance. Part of the difficulty is in determining which of many properties should be satisfied by such a discrete time integration scheme, with additional difficulties in satisfying many properties simultaneously with a single scheme. The desire to use these integration schemes for nonequilibrium simulations and in conjunction with recent nonequilibrium fluctuation theorems adds additional complications. Here, we compare a number of discrete time integration schemes for Langevin dynamics, Continue reading →

Abstract: A deeper understanding of nonequilibrium phenomena is needed to reveal the principles governing natural and synthetic molecular machines. Recent work has shown that when a thermodynamic system is driven from equilibrium then, in the linear response regime, the space of controllable parameters has a Riemannian geometry induced by a generalized friction tensor. Continue reading →

Susanne Still, David A. Sivak, Anthony J. Bell and Gavin E. Crooks, Phys. Rev. Lett., 109, 120604 (2012)

[ Full Text | Journal | Press | arXiv ]

Hat Trick! And Editors’ Suggestion. Also a Nature News item by Philip Ball.

This paper is a melding of ideas about machine learning from Susanne Still and Tony Bell, with ideas from David and I about nonequilibrium thermodynamics. For a molecular scale machine with information processing capabilities, there’s a tradeoff between thermodynamic efficiency, memory and prediction. A prodigious memory allows more accurate prediction of the future, which can be exploited to reduce dissipation. But the persistence of memory is a liability, since information erasure leads to increased dissipation. A thermodynamically optimal machine must balance memory versus prediction by minimizing its nostalgia, the useless information about the past.

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David A. Sivak, Gavin E. Crooks, Phys. Rev. Lett., 108, 190602 (2012)

[ Full Text | Journal | arXiv ]

This is mostly David’s creation, from inception to conclusion, although I like to think I’ve contributed some elegant prose. Essentially, he shows that at the intersection of linear response theory and thermodynamic length analysis, there is a really nice framework for understanding optimal protocols for driving thermodynamic systems. Continue reading →

David A. Sivak, Gavin E. Crooks. Phys. Rev. Lett. 108, 150601 (2012)

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Abstract: A central endeavor of thermodynamics is the measurement of free energy changes. Regrettably, although we can measure the free energy of a system in thermodynamic equilibrium, typically all we can say about the free energy of a non-equilibrium ensemble is that it is larger than that of the same system at equilibrium. Herein, we derive a formally exact expression for the probability distribution of a driven system, which involves path ensemble averages of the work over trajectories of the time-reversed system. From this we find a simple near-equilibrium approximation for the free energy in terms of an excess mean time-reversed work, which can be experimentally measured on real systems. With analysis and computer simulation, we demonstrate the accuracy of our approximations for several simple models.

Version: 0.4 BETA

In a desperate attempt to preserve my own sanity, a survey of probability distributions used to describe a single, continuous, unimodal, univariate random variable.

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Jerome P. Nilmeier, Gavin E. Crooks, David L. D. Minh, and John D. Chodera, Proc. Natl. Acad. Sci. U.S.A. (2011) 108(45) E1009-E1018

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This was a fun paper that originated in a chance, serendipitous conversation between myself, Jerome and John. We realized that several different Monte Carlo sampling techniques that we had each recently worked on could be unified under a common framework by taking inspiration from nonequilibrium thermodynamics. (I am also particularly satisfied with the joke. I firmly believe every scientific paper should contain one joke, lest we take ourselves too seriously. This one is great because it’s hiding in plain sight.)

Abstract: Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also rapidly sample uncorrelated configurations. Continue reading →