Robust estimation of diffusion-optimized ensembles for enhanced sampling

Pengfei Tian; Sigurður Ægir  Jónsson; Jesper Ferkinghoff-Borg; Sergei V . Krivov; Kresten Lindorff-Larsen; Anders  Irbäck; Wouter Krogh Boomsma

doi:10.1021/ct400844x

Robust estimation of diffusion-optimized ensembles for enhanced sampling

Pengfei Tian, Sigurður Ægir Jónsson, Jesper Ferkinghoff-Borg, Sergei V . Krivov, Kresten Lindorff-Larsen, Anders Irbäck, Wouter Krogh Boomsma

10 Citationer (Scopus)

Abstract

The multicanonical, or flat-histogram, method is a common technique to improve the sampling efficiency of molecular simulations. The idea is that free-energy barriers in a simulation can be removed by simulating from a distribution where all values of a reaction coordinate are equally likely, and subsequently reweight the obtained statistics to recover the Boltzmann distribution at the temperature of interest. While this method has been successful in practice, the choice of a flat distribution is not necessarily optimal. Recently, it was proposed that additional performance gains could be obtained by taking the position-dependent diffusion coefficient into account, thus placing greater emphasis on regions diffusing slowly. Although some promising examples of applications of this approach exist, the practical usefulness of the method has been hindered by the difficulty in obtaining sufficiently accurate estimates of the diffusion coefficient. Here, we present a simple, yet robust solution to this problem. Compared to current state-of-the-art procedures, the new estimation method requires an order of magnitude fewer data to obtain reliable estimates, thus broadening the potential scope in which this technique can be applied in practice.

Originalsprog	Engelsk
Tidsskrift	Journal of Chemical Theory and Computation
Vol/bind	10
Udgave nummer	2
Sider (fra-til)	543–553
Antal sider	11
ISSN	1549-9618
DOI	https://doi.org/10.1021/ct400844x
Status	Udgivet - 11 feb. 2014

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Det Natur- og Biovidenskabelige Fakultet

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10.1021/ct400844x

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AU - Tian, Pengfei

AU - Jónsson, Sigurður Ægir

AU - Ferkinghoff-Borg, Jesper

AU - Krivov, Sergei V .

AU - Lindorff-Larsen, Kresten

AU - Irbäck, Anders

AU - Boomsma, Wouter Krogh

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N2 - The multicanonical, or flat-histogram, method is a common technique to improve the sampling efficiency of molecular simulations. The idea is that free-energy barriers in a simulation can be removed by simulating from a distribution where all values of a reaction coordinate are equally likely, and subsequently reweight the obtained statistics to recover the Boltzmann distribution at the temperature of interest. While this method has been successful in practice, the choice of a flat distribution is not necessarily optimal. Recently, it was proposed that additional performance gains could be obtained by taking the position-dependent diffusion coefficient into account, thus placing greater emphasis on regions diffusing slowly. Although some promising examples of applications of this approach exist, the practical usefulness of the method has been hindered by the difficulty in obtaining sufficiently accurate estimates of the diffusion coefficient. Here, we present a simple, yet robust solution to this problem. Compared to current state-of-the-art procedures, the new estimation method requires an order of magnitude fewer data to obtain reliable estimates, thus broadening the potential scope in which this technique can be applied in practice.

AB - The multicanonical, or flat-histogram, method is a common technique to improve the sampling efficiency of molecular simulations. The idea is that free-energy barriers in a simulation can be removed by simulating from a distribution where all values of a reaction coordinate are equally likely, and subsequently reweight the obtained statistics to recover the Boltzmann distribution at the temperature of interest. While this method has been successful in practice, the choice of a flat distribution is not necessarily optimal. Recently, it was proposed that additional performance gains could be obtained by taking the position-dependent diffusion coefficient into account, thus placing greater emphasis on regions diffusing slowly. Although some promising examples of applications of this approach exist, the practical usefulness of the method has been hindered by the difficulty in obtaining sufficiently accurate estimates of the diffusion coefficient. Here, we present a simple, yet robust solution to this problem. Compared to current state-of-the-art procedures, the new estimation method requires an order of magnitude fewer data to obtain reliable estimates, thus broadening the potential scope in which this technique can be applied in practice.

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Robust estimation of diffusion-optimized ensembles for enhanced sampling

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