A self-learning algorithm for biased molecular dynamics

摘要

A new self-learning algorithm for accelerated dynamics, reconnaissance rnmetadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics rnis 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 rnsampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.