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Title: Biasing Smarter, Not Harder, by Partitioning Collective Variables into Families in Parallel Bias Metadynamics

Abstract

Molecular simulations of systems with multiple copies of identical atoms or molecules may require the biasing of numerous, degenerate collective variables (CVs) to accelerate sampling. Recently, a variation of metadynamics (MetaD) named parallel bias metadynamics (PBMetaD) has been shown to make biasing of many CVs more tractable. We extended the PBMetaD scheme so that it partitions degenerate CVs into families that share the same bias potential, consequently expediting convergence of the free-energy landscape. We tested our method, named parallel bias metadynamics with partitioned families, on 3, 21, and 78 CV systems and obtained an approximately proportional increase in convergence speed compared to standard PBMetaD.

Authors:
 [1];  [1];  [2]; ORCiD logo [3]
  1. Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
  2. Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
  3. Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States; Senior Scientist, Pacific Northwest National Laboratory, Richland, Washington, United States
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1508106
Report Number(s):
PNNL-SA-140407
Journal ID: ISSN 1549-9618
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Chemical Theory and Computation
Additional Journal Information:
Journal Volume: 14; Journal Issue: 10; Journal ID: ISSN 1549-9618
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English

Citation Formats

Prakash, Arushi, Fu, Christopher D., Bonomi, Massimiliano, and Pfaendtner, Jim. Biasing Smarter, Not Harder, by Partitioning Collective Variables into Families in Parallel Bias Metadynamics. United States: N. p., 2018. Web. doi:10.1021/acs.jctc.8b00448.
Prakash, Arushi, Fu, Christopher D., Bonomi, Massimiliano, & Pfaendtner, Jim. Biasing Smarter, Not Harder, by Partitioning Collective Variables into Families in Parallel Bias Metadynamics. United States. doi:10.1021/acs.jctc.8b00448.
Prakash, Arushi, Fu, Christopher D., Bonomi, Massimiliano, and Pfaendtner, Jim. Fri . "Biasing Smarter, Not Harder, by Partitioning Collective Variables into Families in Parallel Bias Metadynamics". United States. doi:10.1021/acs.jctc.8b00448.
@article{osti_1508106,
title = {Biasing Smarter, Not Harder, by Partitioning Collective Variables into Families in Parallel Bias Metadynamics},
author = {Prakash, Arushi and Fu, Christopher D. and Bonomi, Massimiliano and Pfaendtner, Jim},
abstractNote = {Molecular simulations of systems with multiple copies of identical atoms or molecules may require the biasing of numerous, degenerate collective variables (CVs) to accelerate sampling. Recently, a variation of metadynamics (MetaD) named parallel bias metadynamics (PBMetaD) has been shown to make biasing of many CVs more tractable. We extended the PBMetaD scheme so that it partitions degenerate CVs into families that share the same bias potential, consequently expediting convergence of the free-energy landscape. We tested our method, named parallel bias metadynamics with partitioned families, on 3, 21, and 78 CV systems and obtained an approximately proportional increase in convergence speed compared to standard PBMetaD.},
doi = {10.1021/acs.jctc.8b00448},
journal = {Journal of Chemical Theory and Computation},
issn = {1549-9618},
number = 10,
volume = 14,
place = {United States},
year = {2018},
month = {8}
}