Issue 56, 2017
From the journal:

RSC Advances

Solubility prediction of gases in polymers based on an artificial neural network: a review

Li Mengshan, ORCID logo *ab   Wu Wei,a   Chen Bingsheng,a   Wu Yana  and  Huang Xingyuanb  

* Corresponding authors

a College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China
E-mail: jcimsli@163.com

b College of Mechanical and Electric Engineering, Nanchang University, Nanchang, Jiangxi, China

Abstract

As an important physical chemistry property, solubility is still a popular research topic. Its theoretical calculation method has developed rapidly. In particular, the artificial neural network (ANN) has attracted the attention of researchers because of its unique nonlinear processing ability. This review provides a brief explanation of the ANN approaches that are most commonly applied to predict gas solubility in polymers, and states the implementation principle, progress, and performance analysis of hybrid ANNs based on the intelligence algorithm. The prospect of solubility prediction based on current research trends is then proposed. This review attempts to analyze the solubility calculation method and provides an insight into and reference for the application of the artificial intelligence method in chemistry and material fields, and can expand in the future because of the increasing number of solubility prediction approaches being introduced.

Graphical abstract: Solubility prediction of gases in polymers based on an artificial neural network: a review

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Article information

DOI
https://doi.org/10.1039/C7RA04200K
Article type
Review Article
Submitted
13 Apr 2017
Accepted
08 Jul 2017
First published
13 Jul 2017
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2017,7, 35274-35282

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Solubility prediction of gases in polymers based on an artificial neural network: a review

L. Mengshan, W. Wei, C. Bingsheng, W. Yan and H. Xingyuan, RSC Adv., 2017, 7, 35274 DOI: 10.1039/C7RA04200K

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