A self-learning algorithm for biased molecular dynamics

Gareth A. Tribello, Michele Ceriotti, Michele Parrinello

Research output: Contribution to journalArticlepeer-review

100 Citations (Scopus)
254 Downloads (Pure)

Abstract

A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.

Original languageEnglish
Pages (from-to)17509-17514
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume107
Issue number41
DOIs
Publication statusPublished - 12 Oct 2010

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