Issue 20, 2021

A review on machine learning algorithms for the ionic liquid chemical space

Abstract

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

Graphical abstract: A review on machine learning algorithms for the ionic liquid chemical space

Supplementary files

Article information

Article type
Review Article
Submitted
19 feb 2021
Accepted
28 apr 2021
First published
06 mei 2021
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2021,12, 6820-6843

A review on machine learning algorithms for the ionic liquid chemical space

S. Koutsoukos, F. Philippi, F. Malaret and T. Welton, Chem. Sci., 2021, 12, 6820 DOI: 10.1039/D1SC01000J

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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