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The Modern Temperature-Accelerated Dynamics Approach

The Modern Temperature-Accelerated Dynamics Approach Accelerated molecular dynamics (AMD) is a class of MD-based methods used to simulate atomistic systems in which the metastable state-to-state evolution is slow compared with thermal vibrations. Temperature-accelerated dynamics (TAD) is a particularly efficient AMD procedure in which the predicted evolution is hastened by elevating the temperature of the system and then recovering the correct state-to-state dynamics at the temperature of interest. TAD has been used to study various materials applications, often revealing surprising behavior beyond the reach of direct MD. This success has inspired several algorithmic performance enhancements, as well as the analysis of its mathematical framework. Recently, these enhancements have leveraged parallel programming techniques to enhance both the spatial and temporal scaling of the traditional approach. We review the ongoing evolution of the modern TAD method and introduce the latest development: speculatively parallel TAD. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annual Review of Chemical and Biomolecular Engineering Annual Reviews

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Publisher
Annual Reviews
Copyright
Copyright © 2016 by Annual Reviews. All rights reserved
ISSN
1947-5438
eISSN
1947-5446
DOI
10.1146/annurev-chembioeng-080615-033608
pmid
26979413
Publisher site
See Article on Publisher Site

Abstract

Accelerated molecular dynamics (AMD) is a class of MD-based methods used to simulate atomistic systems in which the metastable state-to-state evolution is slow compared with thermal vibrations. Temperature-accelerated dynamics (TAD) is a particularly efficient AMD procedure in which the predicted evolution is hastened by elevating the temperature of the system and then recovering the correct state-to-state dynamics at the temperature of interest. TAD has been used to study various materials applications, often revealing surprising behavior beyond the reach of direct MD. This success has inspired several algorithmic performance enhancements, as well as the analysis of its mathematical framework. Recently, these enhancements have leveraged parallel programming techniques to enhance both the spatial and temporal scaling of the traditional approach. We review the ongoing evolution of the modern TAD method and introduce the latest development: speculatively parallel TAD.

Journal

Annual Review of Chemical and Biomolecular EngineeringAnnual Reviews

Published: Jun 7, 2016

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