Materials discovery of ion-selective membranes using artificial intelligence

Reza Maleki; Seyed Mohammadreza Shams; Yasin Mehdizadeh Chellehbari; Sima Rezvantalab; Ahmad Miri Jahromi; Mohsen Asadnia; Rouzbeh Abbassi; Tejraj Aminabhavi; Amir Razmjou

doi:10.1038/s42004-022-00744-x

Materials discovery of ion-selective membranes using artificial intelligence

Commun Chem. 2022 Oct 20;5(1):132. doi: 10.1038/s42004-022-00744-x.

Authors

Affiliations

¹ Department of Chemical Engineering, Shiraz University, Shiraz, Iran.
² Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
³ Department of Chemical Engineering, Sharif University of Technology, Tehran, Iran.
⁴ Renewable Energies Department, Faculty of Chemical Engineering, Urmia University of Technology, 57166-419, Urmia, Iran.
⁵ School of Engineering, Macquarie University, Sydney, NSW, 2109, Australia.
⁶ School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia.
⁷ School of Advanced Sciences, KLE Technological University, Hubballi, Karnataka, 580 031, India.
⁸ Mineral Recovery Research Center (MRRC), School of Engineering, Edith Cowan University, Joondalup, Perth, WA, 6027, Australia. amirr@unsw.edu.au.
⁹ UNESCO Centre for Membrane Science and Technology, School of Chemical Engineering, University of New South Wales, Sydney, NSW, 2052, Australia. amirr@unsw.edu.au.

^# Contributed equally.

Abstract

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.

Publication types

Review