Document Type
Article
Publication Date
5-11-2023
Publication Title
Separation & Purification Reviews
Volume
53
Abstract
The evolving membrane technology integrated with machine learning (ML) algorithms can significantly advance the novel membrane material design and fabrication. Although several studies have reported ML-based assistance in membrane development, none of them have offered a complete analysis of existing ML-assisted membrane fabrication methods from material design perspective. A two-way information gateway is therefore necessary to achieve the desired objective, whereby experienced researchers and data scientists from both sides need to provide valuable insights into novel membrane development process. In this work, we offer a midway platform by providing an overall view and scopes of ML uses in membrane science. This is accomplished by analyzing reported ML-assisted membrane fabrications via lensing through the overall ML development. This work culminates in identifying four crucial factors affecting ML-assisted membrane development: data mining, material functional description, selection of ML models and model interpretation. A future direction is proposed by making specific ML models and descriptors suggestions, in addition to molecular similarity analysis technique and ML-based image processing. We believe the proposed approaches and analyses through our identified lens will prove crucial for the future of ML-assisted membrane material design and development.
Issue
2
First Page
216
Last Page
229
DOI
10.1080/15422119.2023.2212295
ISSN
1542-2127
Rights
This is an Accepted Manuscript of an article published by Taylor & Francis in Separation & Purification Reviews on May 11, 2023, available at: https://doi.org/10.1080/15422119.2023.2212295
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Musabbir J. Talukder, Ali Alshami, Arash Tayyebi, et al.. "Membrane Science Meets Machine Learning: Future and Potential Use in Assisting Membrane Material Design and Fabrication" (2023). Chemical Engineering Faculty Publications. 30.
https://commons.und.edu/che-fac/30