Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning

TL;DR

This paper reviews adaptive sampling methods in molecular dynamics, emphasizing their ability to enhance sampling without biasing forces, especially through recent machine learning advancements.

Contribution

It provides an overview of theoretical principles, summarizes existing methods, and discusses recent machine learning-driven adaptive sampling techniques and their limitations.

Findings

Adaptive sampling preserves thermodynamic ensembles.

Machine learning enhances adaptive sampling efficiency.

Recent methods show promise but have limitations.

Abstract

Molecular Dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long timescales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased and path-sampling methods. In this perspective, we focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved and the sampling is enhanced solely by restarting MD trajectories at particularly chosen seeds, rather than introducing biasing forces. We begin our treatment with an overview of theoretically transparent methods where we discuss principles and guidelines for adaptive sampling. Then, we present a brief summary of select methods that have been applied to realistic systems in the past. Finally, we discuss recent advances in adaptive sampling methodology powered by machine learning techniques as well as their shortcomings.