Journal Article

Collective variable-based enhanced sampling and machine learning

Chen, Ming

2021

Overview

Collective variable-based enhanced sampling methods have been widely used to study thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced sampling methods are affected by two factors: constructing appropriate collective variables for enhanced sampling and generating accurate free energy surfaces. Recently, many machine learning techniques have been developed to improve the quality of collective variables and the accuracy of free energy surfaces. Although machine learning has achieved great successes in improving enhanced sampling methods, there are still many challenges and open questions. In this perspective, we shall review recent developments on integrating machine learning techniques and collective variable-based enhanced sampling approaches. We also discuss challenges and future research directions including generating kinetic information, exploring high-dimensional free energy surfaces, and efficiently sampling all-atom configurations. Graphic abstract

• Publisher: Springer Berlin Heidelberg,Springer,Springer Nature B.V
• Subject: Complex Systems / Condensed Matter Physics / Fluid- and Aerodynamics / Free energy / Machine learning / Physics / Physics and Astronomy / Recent Progress and Emerging Trends in Molecular Dynamics / Sampling methods / Solid State Physics / Thermodynamic properties / Thermodynamics / Topical Review - Computational Methods