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

Diego E. Kleiman, Hassan Nadeem, Diwakar Shukla

Research output: Contribution to journalReview articlepeer-review

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 time scales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased sampling 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 deep learning techniques, as well as their shortcomings.

Original languageEnglish (US)
Pages (from-to)10669-10681
Number of pages13
JournalJournal of Physical Chemistry B
Volume127
Issue number50
DOIs
StatePublished - Dec 21 2023

ASJC Scopus subject areas

  • Materials Chemistry
  • Surfaces, Coatings and Films
  • Physical and Theoretical Chemistry

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