Rethinking Metadynamics: from bias potentials to probability distributions

TL;DR

This paper introduces a new perspective on metadynamics, focusing on probability distribution reconstruction rather than bias potentials, resulting in faster convergence, robustness, and better control over phase space exploration.

Contribution

It shifts the focus from bias potentials to probability distributions in metadynamics, improving convergence speed and ease of use with minimal parameters.

Findings

Significant speed-up in convergence compared to traditional methods

Robustness in high-dimensional and suboptimal collective variable scenarios

Straightforward reweighting scheme for unbiased statistics

Abstract

Metadynamics is an enhanced sampling method of great popularity, based on the on-the-fly construction of a bias potential that is function of a selected number of collective variables. We propose here a change in perspective that shifts the focus from the bias to the probability distribution reconstruction, while keeping some of the key characteristics of metadynamics, such as the flexible on-the-fly adjustments to the free energy estimate. The result is an enhanced sampling method that presents a drastic improvement in convergence speed, especially when dealing with suboptimal and/or multidimensional sets of collective variables. The method is especially robust and easy to use, in fact it requires only few simple parameters to be set, and it has a straightforward reweighting scheme to recover the statistics of the unbiased ensemble. Furthermore it gives more control on the desired exploration of the phase space, since the deposited bias is not allowed to grow indefinitely and it does not push the simulation to uninteresting high free energy regions. We demonstrate the performance of the method in a number of representative examples.