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

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.

References in corpus (3)

• Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
• A Variational Approach to Enhanced Sampling and Free Energy Calculations
• Metadynamics with adaptive Gaussians

Cited by in corpus (13)

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• Rare Event Kinetics from Adaptive Bias Enhanced Sampling
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• Characterizing metastable states with the help of machine learning
• Collective Variables for Conformational Polymorphism in Molecular Crystals
• Deep learning the slow modes for rare events sampling
• Exploration vs Convergence Speed in Adaptive-bias Enhanced Sampling
• A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes
• Reweighted Jarzynski sampling: Acceleration of rare events and free energy calculation with a bias potential learned from nonequilibrium work
• Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials
• Improving the Exploration of High Dimensional Free Energy Landscape by a Combination of Temperature Accelerated Sliced Sampling and Parallel Biasing