# Making the best of a bad situation: a multiscale approach to free energy calculation

## TL;DR

This paper introduces a multiscale approach combining exploratory metadynamics and variationally enhanced sampling to improve free energy calculations in complex systems where optimal collective variables are hard to identify.

## Contribution

It proposes a novel two-step method that accelerates convergence by addressing residual slow modes, enhancing free energy calculations in challenging systems.

## Key findings

- Significantly speeds up convergence in free energy calculations.
- Effective for complex systems with non-ideal collective variables.
- Combines exploratory metadynamics with variationally enhanced sampling.

## Abstract

Many enhanced sampling techniques rely on the identification of a number of collective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space one is then able to sample efficiently and reconstruct the free energy landscape. In methods like metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal with the non-ideal situation of a system in which some slow modes are not accelerated. We propose a two-step approach in which, by taking into account the residual multiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free energy difference between metastable states, based on the recently proposed variationally enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.