Literature DB >> 25215968

Variational approach to enhanced sampling and free energy calculations.

Omar Valsson1, Michele Parrinello1.   

Abstract

The ability of widely used sampling methods, such as molecular dynamics or Monte Carlo simulations, to explore complex free energy landscapes is severely hampered by the presence of kinetic bottlenecks. A large number of solutions have been proposed to alleviate this problem. Many are based on the introduction of a bias potential which is a function of a small number of collective variables. However constructing such a bias is not simple. Here we introduce a functional of the bias potential and an associated variational principle. The bias that minimizes the functional relates in a simple way to the free energy surface. This variational principle can be turned into a practical, efficient, and flexible sampling method. A number of numerical examples are presented which include the determination of a three-dimensional free energy surface. We argue that, beside being numerically advantageous, our variational approach provides a convenient and novel standpoint for looking at the sampling problem.

Year:  2014        PMID: 25215968     DOI: 10.1103/PhysRevLett.113.090601

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  28 in total

1.  Locating landmarks on high-dimensional free energy surfaces.

Authors:  Ming Chen; Tang-Qing Yu; Mark E Tuckerman
Journal:  Proc Natl Acad Sci U S A       Date:  2015-03-03       Impact factor: 11.205

2.  Neural networks-based variationally enhanced sampling.

Authors:  Luigi Bonati; Yue-Yu Zhang; Michele Parrinello
Journal:  Proc Natl Acad Sci U S A       Date:  2019-08-15       Impact factor: 11.205

3.  Coarse graining from variationally enhanced sampling applied to the Ginzburg-Landau model.

Authors:  Michele Invernizzi; Omar Valsson; Michele Parrinello
Journal:  Proc Natl Acad Sci U S A       Date:  2017-03-14       Impact factor: 11.205

4.  Spectral gap optimization of order parameters for sampling complex molecular systems.

Authors:  Pratyush Tiwary; B J Berne
Journal:  Proc Natl Acad Sci U S A       Date:  2016-02-29       Impact factor: 11.205

5.  Signatures of a liquid-liquid transition in an ab initio deep neural network model for water.

Authors:  Thomas E Gartner; Linfeng Zhang; Pablo M Piaggi; Roberto Car; Athanassios Z Panagiotopoulos; Pablo G Debenedetti
Journal:  Proc Natl Acad Sci U S A       Date:  2020-10-02       Impact factor: 11.205

Review 6.  RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview.

Authors:  Jiří Šponer; Giovanni Bussi; Miroslav Krepl; Pavel Banáš; Sandro Bottaro; Richard A Cunha; Alejandro Gil-Ley; Giovanni Pinamonti; Simón Poblete; Petr Jurečka; Nils G Walter; Michal Otyepka
Journal:  Chem Rev       Date:  2018-01-03       Impact factor: 60.622

7.  Metadynamics combined with auxiliary density functional and density functional tight-binding methods: alanine dipeptide as a case study.

Authors:  Jerome Cuny; Kseniia Korchagina; Chemseddine Menakbi; Tzonka Mineva
Journal:  J Mol Model       Date:  2017-02-15       Impact factor: 1.810

8.  Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin.

Authors:  Patrick Shaffer; Omar Valsson; Michele Parrinello
Journal:  Proc Natl Acad Sci U S A       Date:  2016-01-19       Impact factor: 11.205

9.  Unsupervised Learning Methods for Molecular Simulation Data.

Authors:  Aldo Glielmo; Brooke E Husic; Alex Rodriguez; Cecilia Clementi; Frank Noé; Alessandro Laio
Journal:  Chem Rev       Date:  2021-05-04       Impact factor: 60.622

10.  Kinetics of protein-ligand unbinding: Predicting pathways, rates, and rate-limiting steps.

Authors:  Pratyush Tiwary; Vittorio Limongelli; Matteo Salvalaglio; Michele Parrinello
Journal:  Proc Natl Acad Sci U S A       Date:  2015-01-20       Impact factor: 11.205

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