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

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

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

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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.

Original language	English
Article number	132
Pages (from-to)	1-13
Number of pages	13
Journal	Communications Chemistry
Volume	5
DOIs	https://doi.org/10.1038/s42004-022-00744-x
Publication status	Published - 2022

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Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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title = "Materials discovery of ion-selective membranes using artificial intelligence",

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.",

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doi = "10.1038/s42004-022-00744-x",

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AU - Shams, Seyed Mohammadreza

AU - Chellehbari, Yasin Mehdizadeh

AU - Rezvantalab, Sima

AU - Jahromi, Ahmad Miri

AU - Asadnia, Mohsen

AU - Abbassi, Rouzbeh

AU - Aminabhavi, Tejraj

AU - Razmjou, Amir

N1 - Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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AB - 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.

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JO - Communications Chemistry

JF - Communications Chemistry

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ER -

Materials discovery of ion-selective membranes using artificial intelligence

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