A Variational Approach to Enhanced Sampling and Free Energy Calculations

TL;DR

This paper introduces a variational method for enhanced sampling in molecular simulations, enabling more efficient exploration of free energy landscapes by optimizing a bias potential functional.

Contribution

It presents a novel variational principle for constructing bias potentials, improving sampling efficiency and providing new insights into free energy calculations.

Findings

Successfully determined a three-dimensional free energy surface

Demonstrated numerical efficiency over existing methods

Provided a flexible framework for sampling complex landscapes

Abstract

The ability of widely used sampling methods, such as molecular dynamics or Monte Carlo, to explore complex free energy landscapes is severely hampered by the presence of kinetic bottlenecks. A large number of solutions have been proposed to alleviate this problem. Many are based on the introduction of a bias potential which is a function of a small number of collective variable. However constructing such a bias is not simple. Here we introduce a functional of the bias potential and an associated variational principle. The bias that minimizes the functional relates in a simple way to the free energy surface. This variational principle can be turned into a practical, efficient and flexible sampling method. A number of numerical examples are presented which include the determination of a three dimensional free energy surface. We argue that, beside being numerically advantageous, our variational approach provides a convenient standpoint for looking with novel eyes at the sampling problem.