Robust Estimation of Diffusion-Optimized Ensembles for Enhanced Sampling
Tian, Pengfei ; Jonsson, Sigurdur LU ; Ferkinghoff-Borg, Jesper ; Krivov, Sergi V. ; Lindorff-Larsen, Kresten ; Irbäck, Anders LU
and Boomsma, Wouter
(2014)
In Journal of Chemical Theory and Computation
10(2).
p.543-553
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4368401
- author
- Tian, Pengfei
; Jonsson, Sigurdur
LU
; Ferkinghoff-Borg, Jesper
; Krivov, Sergi V.
; Lindorff-Larsen, Kresten
; Irbäck, Anders
LU
and Boomsma, Wouter
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Chemical Theory and Computation
- volume
- 10
- issue
- 2
- pages
- 543 - 553
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- wos:000331342400007
- scopus:84894149420
- pmid:26580031
- ISSN
- 1549-9618
- DOI
- 10.1021/ct400844x
- language
- English
- LU publication?
- yes
- id
- 8735235b-cb27-4ea7-b9c1-786365a43f79 (old id 4368401)
- date added to LUP
- 2016-04-01 10:27:27
- date last changed
- 2024-01-06 17:00:28
@article{8735235b-cb27-4ea7-b9c1-786365a43f79,
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.}},
author = {{Tian, Pengfei and Jonsson, Sigurdur and Ferkinghoff-Borg, Jesper and Krivov, Sergi V. and Lindorff-Larsen, Kresten and Irbäck, Anders and Boomsma, Wouter}},
issn = {{1549-9618}},
language = {{eng}},
number = {{2}},
pages = {{543--553}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Journal of Chemical Theory and Computation}},
title = {{Robust Estimation of Diffusion-Optimized Ensembles for Enhanced Sampling}},
url = {{http://dx.doi.org/10.1021/ct400844x}},
doi = {{10.1021/ct400844x}},
volume = {{10}},
year = {{2014}},
}