Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method

TLDR

The method unifies metadynamics and canonical sampling as limiting cases. The authors present a method to determine free‑energy dependence on selected collective variables via an adaptive bias. The algorithm tunes simulation parameters to focus computational effort on relevant regions and is tested on reconstructing the alanine dipeptide free‑energy landscape. Convergence and errors are rigorously and easily controlled.

Abstract

We present a method for determining the free-energy dependence on a selected number of collective variables using an adaptive bias. The formalism provides a unified description which has metadynamics and canonical sampling as limiting cases. Convergence and errors can be rigorously and easily controlled. The parameters of the simulation can be tuned so as to focus the computational effort only on the physically relevant regions of the order parameter space. The algorithm is tested on the reconstruction of an alanine dipeptide free-energy landscape.