title: A self-learning algorithm for biased molecular dynamics.
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.
authors.affiliation: Computational Science, Department of Chemistry and Applied Biosciences, Eidgenössiche Technische Hochschule Zurich, Università della Svizzera Italiana Campus, Via Giuseppe Buffi 13 C-6900 Lugano, Switzerland.
authors.forename: Gareth A
authors.initials: GA
authors.lastname: Tribello
authors.forename: Michele
authors.initials: M
authors.lastname: Ceriotti
authors.forename: Michele
authors.initials: M
authors.lastname: Parrinello
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D008968:Molecular Conformation; D056004:Molecular Dynamics Simulation; D025341:Principal Component Analysis