Efficient water desalination with graphene nanopores obtained using artificial intelligence

Abstract Two-dimensional nanomaterials, such as graphene, have been extensively studied because of their outstanding physical properties. Structure and topology of nanopores on such materials can be important for their performances in real-world engineering applications, like water desalination. How...

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Autores principales:	Yuyang Wang, Zhonglin Cao, Amir Barati Farimani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/40f2f0a2febe44718a32e1a7b7235e2e
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spelling	oai:doaj.org-article:40f2f0a2febe44718a32e1a7b7235e2e2021-12-02T17:01:31ZEfficient water desalination with graphene nanopores obtained using artificial intelligence10.1038/s41699-021-00246-92397-7132https://doaj.org/article/40f2f0a2febe44718a32e1a7b7235e2e2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41699-021-00246-9https://doaj.org/toc/2397-7132Abstract Two-dimensional nanomaterials, such as graphene, have been extensively studied because of their outstanding physical properties. Structure and topology of nanopores on such materials can be important for their performances in real-world engineering applications, like water desalination. However, discovering the most efficient nanopores often involves a very large number of experiments or simulations that are expensive and time-consuming. In this work, we propose a data-driven artificial intelligence (AI) framework for discovering the most efficient graphene nanopore for water desalination. Via a combination of deep reinforcement learning (DRL) and convolutional neural network (CNN), we are able to rapidly create and screen thousands of graphene nanopores and select the most energy-efficient ones. Molecular dynamics (MD) simulations on promising AI-created graphene nanopores show that they have higher water flux while maintaining rival ion rejection rate compared to the normal circular nanopores. Irregular shape with rough edges geometry of AI-created pores is found to be the key factor for their high water desalination performance. Ultimately, this study shows that AI can be a powerful tool for nanomaterial design and screening.Yuyang WangZhonglin CaoAmir Barati FarimaniNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ChemistryQD1-999ENnpj 2D Materials and Applications, Vol 5, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Chemistry QD1-999
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Chemistry QD1-999 Yuyang Wang Zhonglin Cao Amir Barati Farimani Efficient water desalination with graphene nanopores obtained using artificial intelligence
description	Abstract Two-dimensional nanomaterials, such as graphene, have been extensively studied because of their outstanding physical properties. Structure and topology of nanopores on such materials can be important for their performances in real-world engineering applications, like water desalination. However, discovering the most efficient nanopores often involves a very large number of experiments or simulations that are expensive and time-consuming. In this work, we propose a data-driven artificial intelligence (AI) framework for discovering the most efficient graphene nanopore for water desalination. Via a combination of deep reinforcement learning (DRL) and convolutional neural network (CNN), we are able to rapidly create and screen thousands of graphene nanopores and select the most energy-efficient ones. Molecular dynamics (MD) simulations on promising AI-created graphene nanopores show that they have higher water flux while maintaining rival ion rejection rate compared to the normal circular nanopores. Irregular shape with rough edges geometry of AI-created pores is found to be the key factor for their high water desalination performance. Ultimately, this study shows that AI can be a powerful tool for nanomaterial design and screening.
format	article
author	Yuyang Wang Zhonglin Cao Amir Barati Farimani
author_facet	Yuyang Wang Zhonglin Cao Amir Barati Farimani
author_sort	Yuyang Wang
title	Efficient water desalination with graphene nanopores obtained using artificial intelligence
title_short	Efficient water desalination with graphene nanopores obtained using artificial intelligence
title_full	Efficient water desalination with graphene nanopores obtained using artificial intelligence
title_fullStr	Efficient water desalination with graphene nanopores obtained using artificial intelligence
title_full_unstemmed	Efficient water desalination with graphene nanopores obtained using artificial intelligence
title_sort	efficient water desalination with graphene nanopores obtained using artificial intelligence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/40f2f0a2febe44718a32e1a7b7235e2e
work_keys_str_mv	AT yuyangwang efficientwaterdesalinationwithgraphenenanoporesobtainedusingartificialintelligence AT zhonglincao efficientwaterdesalinationwithgraphenenanoporesobtainedusingartificialintelligence AT amirbaratifarimani efficientwaterdesalinationwithgraphenenanoporesobtainedusingartificialintelligence
_version_	1718382078578393088

Efficient water desalination with graphene nanopores obtained using artificial intelligence

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