Metadynamics with adaptive Gaussians

TL;DR

This paper introduces an adaptive Gaussian approach in metadynamics, adjusting Gaussian variances dynamically to improve free-energy surface reconstruction, with demonstrated benefits in accuracy and convergence speed.

Contribution

It proposes two methods for adaptive Gaussian variances in metadynamics and a new free-energy estimator suitable for these adaptive biases.

Findings

Adaptive Gaussians improve free-energy surface accuracy.

The new estimator enhances convergence speed.

Method demonstrated on alanine dipeptide simulation.

Abstract

Metadynamics is an established sampling method aimed at reconstructing the free-energy surface relative to a set of appropriately chosen collective variables. In standard metadynamics the free-energy surface is filled by the addition of Gaussian potentials of pre-assigned and typically diagonal covariance. Asymptotically the free-energy surface is proportional to the bias deposited. Here we consider the possibility of using Gaussians whose variance is adjusted on the fly to the local properties of the free-energy surface. We suggest two different prescriptions: one is based on the local diffusivity and the other on the local geometrical properties. We further examine the problem of extracting the free-energy surface when using adaptive Gaussians. We show that the standard relation between the bias and the free energy does not hold. In the limit of narrow Gaussians an explicit correction can be evaluated. In the general case we propose to use instead a relation between bias and free energy borrowed from umbrella sampling. This relation holds for all kinds of incrementally deposited bias. We illustrate on the case of alanine dipeptide the advantage of using adaptive Gaussians in conjunction with the new free-energy estimator both in terms of accuracy and speed of convergence.