Issue 1, 2021

Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks

Abstract

In this review, we examine how machine learning (ML) can build on molecular simulation (MS) algorithms to advance tremendously our ability to predict the thermodynamic properties of a wide range of systems. The key thermodynamic properties that govern the evolution of a system and the outcome of a process include the entropy, the Helmholtz and the Gibbs free energy. However, their determination through advanced molecular simulation algorithms has remained challenging, since such methods are extremely computationally intensive. Combining MS with ML provides a solution that overcomes such challenges and, in turn, accelerates discovery through the rapid prediction of free energies. After presenting a brief overview of combined MS–ML protocols, we review how these approaches allow for the accurate prediction of these thermodynamic functions and, more broadly, of free energy landscapes for molecular and biological systems. We then discuss extensions of this approach to systems relevant to energy and environmental applications, i.e. gas storage and separation in nanoporous materials, such as metal–organic frameworks and covalent organic frameworks. We finally show in the last part of the review how ML models can suggest new ways to explore free energy landscapes, identify novel pathways and provide new insight into assembly processes.

Graphical abstract: Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks

Article information

Article type
Review Article
Submitted
22 Sep 2020
Accepted
23 Nov 2020
First published
23 Nov 2020

Mol. Syst. Des. Eng., 2021,6, 52-65

Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks

C. Desgranges and J. Delhommelle, Mol. Syst. Des. Eng., 2021, 6, 52 DOI: 10.1039/D0ME00134A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements