2022/01/01 by Megan C. Engel, Jamie A. Smith, Jamie Smith +4 · 12 citations
Computer Science · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Applied Physics (physics.app-ph) #Biological Physics (physics.bio-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Spectroscopy and Quantum Chemical Studies #physics.app-ph #physics.bio-ph #physics.comp-ph
paper · pdf · doi:10.48550/arxiv.2201.00098
14 pages, 3 figures plus supplemental info (12 pages, 10 figures)
arxiv created 2022/01/01 · openalex publication_date 2022/01/01 · arxiv updated 2022/01/04 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
Controlling the evolution of nonequilibrium systems to minimize dissipated heat or work is a key goal for designing nanodevices, both in nanotechnology and biology. Progress in computing optimal protocols has thus far been limited to either simple systems or near-equilibrium evolution. Here, we present an approach for computing optimal protocols based on automatic differentiation. Our methodology is applicable to complex systems and multidimensional protocols and is valid arbitrarily far from equilibrium. We validate our method by reproducing theoretical optimal protocols for a Brownian particle in a time-varying harmonic trap. We also compute departures from near-equilibrium behaviour for magnetization reversal on an Ising lattice and for barrier crossing driven by a harmonic trap, which has been used to represent a range of biological processes including biomolecular unfolding reactions. Algorithms based on automatic differentiation outperform the near-equilibrium theory for far-from-equilibrium magnetization reversal and driven barrier crossing. The optimal protocol for crossing an energy landscape barrier of 10kT is found to hasten the approach to, and slow the departure from, the barrier region compared to the near-equilibrium theoretical protocol.