A self-learning algorithm for biased molecular dynamics

Details A self-learning algorithm for biased molecular dynamics A self-learning algorithm for biased molecular dynamics

2010-09-07

arxiv.org/abs/1009.1236v1

PNAS 2010 107 (41) 17509-17514

Physics

Computational Physics

6 pages, 5 figures + 9 pages of supplementary information

Scientific paper

10.1073/pnas.1011511107

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.

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