Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation
[Image: see text] The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of st...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8154255/ https://www.ncbi.nlm.nih.gov/pubmed/33914526 http://dx.doi.org/10.1021/acs.jcim.1c00191 |
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author | Altintas, Cigdem Altundal, Omer Faruk Keskin, Seda Yildirim, Ramazan |
author_facet | Altintas, Cigdem Altundal, Omer Faruk Keskin, Seda Yildirim, Ramazan |
author_sort | Altintas, Cigdem |
collection | PubMed |
description | [Image: see text] The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure–performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful. |
format | Online Article Text |
id | pubmed-8154255 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-81542552021-05-27 Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation Altintas, Cigdem Altundal, Omer Faruk Keskin, Seda Yildirim, Ramazan J Chem Inf Model [Image: see text] The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure–performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful. American Chemical Society 2021-04-29 2021-05-24 /pmc/articles/PMC8154255/ /pubmed/33914526 http://dx.doi.org/10.1021/acs.jcim.1c00191 Text en © 2021 The Authors. Published by American Chemical Society Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Altintas, Cigdem Altundal, Omer Faruk Keskin, Seda Yildirim, Ramazan Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation |
title | Machine Learning Meets with Metal Organic Frameworks
for Gas Storage and Separation |
title_full | Machine Learning Meets with Metal Organic Frameworks
for Gas Storage and Separation |
title_fullStr | Machine Learning Meets with Metal Organic Frameworks
for Gas Storage and Separation |
title_full_unstemmed | Machine Learning Meets with Metal Organic Frameworks
for Gas Storage and Separation |
title_short | Machine Learning Meets with Metal Organic Frameworks
for Gas Storage and Separation |
title_sort | machine learning meets with metal organic frameworks
for gas storage and separation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8154255/ https://www.ncbi.nlm.nih.gov/pubmed/33914526 http://dx.doi.org/10.1021/acs.jcim.1c00191 |
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