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

Arushi Prakash; Christopher D Fu; Massimiliano Bonomi; Jim Pfaendtner

doi:10.1021/acs.jctc.8b00448

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

J Chem Theory Comput. 2018 Oct 9;14(10):4985-4990. doi: 10.1021/acs.jctc.8b00448. Epub 2018 Sep 6.

Authors

Arushi Prakash¹, Christopher D Fu¹, Massimiliano Bonomi², Jim Pfaendtner^{1

3}

Affiliations

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 Senior Scientist , Pacific Northwest National Laboratory , Richland , Washington , United States.

PMID: 30075630
DOI: 10.1021/acs.jctc.8b00448

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.