A new era of modeling MOF-based membranes: Cooperation of theory and data science

Name: A new era of modeling MOF-based membranes: Cooperation of theory and data science
Author: Demir, Hakan, Keskin, S.

İsim	A new era of modeling MOF-based membranes: Cooperation of theory and data science
Yazar	Demir, Hakan, Keskin, S.
Basım Tarihi:	2024-01
Basım Yeri	- Wiley
Konu	Gas separation, Machine learning, Membranes, Mixed matrix membranes, MOFs, Molecular simulation
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1438-7492
Kayıt Numarası	bf56a953-5ad9-4472-b16a-30f89de5b586
Lokasyon	Natural and Mathematical Sciences
Tarih	2024-01
Notlar	European Union’s Horizon 2020 ; TÜBİTAK
Örnek Metin	Membrane-based separation can offer significant energy savings over conventional separation methods. Given their highly customizable and porous structures, metal–organic frameworks- (MOFs) are considered as next-generation membrane materials that can bring about high separation performance and energy efficiency in various separation applications. Yet, the enormously large number of possible MOF structures necessitates the development and implementation of efficient modeling approaches to expedite the design, discovery, and selection of optimal MOF-based membranes via directing the experimental efforts, time, and resources to the potentially useful membrane materials. With the recent developments in the field of atomic simulations and artificial intelligence methods, a new era of membrane modeling has started. This review focuses on the recent advances made and key strategies used in the modeling of MOF-based membranes and highlight the huge potential of combining atomistic modeling of MOFs with machine learning to explore very large number of MOF membranes and MOF/polymer composite membranes for gas separation. Opportunities and challenges related to the implementation of data-driven approaches to extract useful structure–property relations of MOF-based membranes and to produce design principles for the high-performing MOF-based membranes are discussed.
DOI	10.1002/mame.202300225
Cilt	309

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A new era of modeling MOF-based membranes: Cooperation of theory and data science

Yazar Demir, Hakan, Keskin, S.

Basım Tarihi 2024-01

Basım Yeri - Wiley

Konu Gas separation, Machine learning, Membranes, Mixed matrix membranes, MOFs, Molecular simulation

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1438-7492

Kayıt Numarası bf56a953-5ad9-4472-b16a-30f89de5b586

Lokasyon Natural and Mathematical Sciences

Tarih 2024-01

Notlar European Union’s Horizon 2020 ; TÜBİTAK

Örnek Metin Membrane-based separation can offer significant energy savings over conventional separation methods. Given their highly customizable and porous structures, metal–organic frameworks- (MOFs) are considered as next-generation membrane materials that can bring about high separation performance and energy efficiency in various separation applications. Yet, the enormously large number of possible MOF structures necessitates the development and implementation of efficient modeling approaches to expedite the design, discovery, and selection of optimal MOF-based membranes via directing the experimental efforts, time, and resources to the potentially useful membrane materials. With the recent developments in the field of atomic simulations and artificial intelligence methods, a new era of membrane modeling has started. This review focuses on the recent advances made and key strategies used in the modeling of MOF-based membranes and highlight the huge potential of combining atomistic modeling of MOFs with machine learning to explore very large number of MOF membranes and MOF/polymer composite membranes for gas separation. Opportunities and challenges related to the implementation of data-driven approaches to extract useful structure–property relations of MOF-based membranes and to produce design principles for the high-performing MOF-based membranes are discussed.

DOI 10.1002/mame.202300225

Cilt 309