RT Journal Article
SR Electronic
T1 On-line Optimization of Hamiltonian Replica Exchange Simulations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 228262
DO 10.1101/228262
A1 Justin L. MacCallum
A1 Mir Ishruna Muniyat
A1 Kari Gaalswyk
YR 2017
UL http://biorxiv.org/content/early/2017/12/03/228262.abstract
AB Replica exchange is a widely used sampling strategy in molecular simulation. While a variety of methods exist for optimizing temperature replica exchange, less is known about how to optimize more general Hamiltonian replica exchange simulations. We present an algorithm for the on-line optimization of both temperature and Hamiltonian replica exchange simulations that draws on techniques from the optimization of deep neural networks in machine learning. We optimize a heuristic-based objective function capturing the efficiency of replica exchange. Our approach is general, and has several desirable properties, including: (1) it makes few assumptions about the system of interest; (2) optimization occurs on-line wihout the requirement of pre-simulation; and (3) it readily generalizes to systems where there are multiple control parameters per replica. We explore some general properties of the algorithm on a simple harmonic oscillator system, and demonstrate its effectiveness on a more complex data-guided protein folding simulation.