A self-learning algorithm for biased molecular dynamics

TL;DR

This paper introduces reconnaissance metadynamics, a self-learning algorithm that accelerates molecular dynamics simulations by adaptively constructing bias potentials from multiple collective coordinates, improving sampling efficiency in complex systems.

Contribution

The paper presents a novel self-learning algorithm capable of handling many collective coordinates for enhanced sampling in molecular dynamics.

Findings

Effective acceleration in chemical systems demonstrated

Adaptive bias potential construction from trajectory analysis

Applicable to physics and biological systems

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.