Calculation of free energy landscapes: A Histogram Reweighted Metadynamics approach

TL;DR

This paper introduces a histogram reweighted metadynamics method for efficiently calculating free energy landscapes, capable of accelerating rare events and extending to auxiliary variables, demonstrated on peptide examples.

Contribution

The paper presents a novel histogram reweighted metadynamics approach that improves free energy landscape calculations and can be extended to auxiliary variables without extra numerical effort.

Findings

Effective calculation of free energy landscapes demonstrated on peptides.

Method accelerates rare events compared to traditional approaches.

Empirical formula predicts computational cost of the method.

Abstract

We present an efficient method for the calculation of free energy landscapes. Our approach involves a history dependent bias potential which is evaluated on a grid. The corresponding free energy landscape is constructed via a histogram reweighting procedure a posteriori. Due to the presence of the bias potential, it can be also used to accelerate rare events. In addition, the calculated free energy landscape is not restricted to the actual choice of collective variables and can in principle be extended to auxiliary variables of interest without further numerical effort. The applicability is shown for several examples. We present numerical results for the alanine dipeptide and the Met-Enkephalin in explicit solution to illustrate our approach. Furthermore we derive an empirical formula that allows the prediction of the computational cost for the ordinary metadynamics variant in comparison to our approach which is validated by a dimensionless representation.