期刊缩写 WIRES CLIM CHANGE
期刊全称 WIREs Computational Molecular Science 导线计算分子科学
期刊ISSN 1759-0876
2013-2014最新影响因子 9.041
期刊官方网站 http://wires.wiley.com/WileyCDA/WiresArticle/wisId-WCMS1086.html
期刊投稿网址
通讯方式
涉及的研究方向 CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
出版国家 ENGLAND
出版周期
出版年份 2011
年文章数 40
Editor-in-Chief: Peter R. Schreiner
Impact Factor: 9.041
ISI Journal Citation Reports ? Ranking: 2013: 1/52 (Mathematical & Computational Biology); 13/148 (Chemistry Multidisciplinary)
Online ISSN: 1759-0884
Associated Title(s): Journal of Computational Chemistry
This important new forum promotes cross-disciplinary research on computational chemistry, biochemistry and materials science.
An authoritative, encyclopedic resource addressing key topics from diverse research perspectives.
Content is fully citable, qualifying for abstracting, indexing, and ISI ranking.
*NEW: Now indexed by TRSI (formerly ISI), Scopus and CAS: Chemical Abstract Services!
Topical coverage includes:
Electronic Structure Theory
Molecular and Statistical Mechanics
Computer and Information Science
Computational Chemistry
Theoretical and Physical Chemistry
Polarizable continuum model
Advanced Review
BENEDETTA MENNUCCI
Published Online: Jan 17 2012
DOI: 10.1002/wcms.1086
The polarizable continuum model (PCM) is a computational method originally formulated 30 years ago but still today it represents one of the most successful examples among continuum solvation models. Such a success is mainly because of the continuous improvements, both in terms of computational efficiency and generality, made by all the people involved in the PCM project. The result of these efforts is that nowadays, PCM, with all its different variants, is the default choice in many computational codes to couple a quantum–mechanical (QM) description of a molecular system with a continuum description of the environment. In this review, a brief presentation of the main methodological and computational aspects of the method will be given together with an analysis of strengths and critical issues of its coupling with different QM methods. Finally, some examples of applications will be presented and discussed to show the potentialities of PCM in describing the effects of environments of increasing complexity. ? 2012 John Wiley & Sons, Ltd.
Umbrella sampling
Advanced Review
JOHANNES K?STNER
Published Online: May 26 2011
DOI: 10.1002/wcms.66
The calculation of free‐energy differences is one of the main challenges in computational biology and biochemistry. Umbrella sampling, biased molecular dynamics (MD), is one of the methods that provide free energy along a reaction coordinate. Here, the method is derived in a historic overview and is compared with related methods like thermodynamic integration, slow growth, steered MD, or the Jarzynski‐based fast‐growth technique. In umbrella sampling, bias potentials along a (one‐ or more‐dimensional) reaction coordinate drive a system from one thermodynamic state to another (e.g., reactant and product). The intermediate steps are covered by a series of windows, at each of which an MD simulation is performed. The bias potentials can have any functional form. Often, harmonic potentials are used for their simplicity. From the sampled distribution of the system along the reaction coordinate, the change in free energy in each window can be calculated. The windows are then combined by methods like the weighted histogram analysis method or umbrella integration. If the bias potential is adapted to result in an even distribution between the end states, then this whole range can be spanned by one window (adaptive‐bias umbrella sampling). In this case, the free‐energy change is directly obtained from the bias. The sampling in each window can be improved by replica exchange methods; either by exchange between successive windows or by running additional simulations at higher temperatures.
? 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 932–942 DOI: 10.1002/wcms.66
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