Abstract

Many systems like for instance biomolecules are characterized by complex and rough potential energy surfaces and their behavior often relies on a delicate balance between enthalpy and entropy. Is such a cases a simulation method has to find ways of addressing both the issue of accurately sampling phase space and that of estimating free energies. Many techniques have been proposed to this effect. Here we discuss metadynamics which is a powerful technique for enhancing sampling in molecular dynamics simulations and reconstructing the free energy surface as a function of few selected degrees of freedom, often referred to as collective variables (CVs). In metadynamics, sampling is accelerated by a history- dependent bias potential, which is adaptively constructed in the space of the CVs. Since its first appearance, significant improvements have been made to the original algorithm, leading to an efficient, flexible and accurate method that has found many successful applications in several domains of science. Here we discuss first the theory underlying metadynamics and its recent developments. In particular we focus on the crucial issue of choosing an appropriate set of CVs and on the possible strategies to alleviate this difficulty. In a very recent development that we call reconnaissance metadynamics, we have applied methods borrowed from machine learning to reconstruct the CVs. The principle and first applications of this very new approach will be also discussed.