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Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution

[Image: see text] Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for...

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Autores principales: Ongari, Daniele, Talirz, Leopold, Smit, Berend
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2020
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706098/
https://www.ncbi.nlm.nih.gov/pubmed/33274268
http://dx.doi.org/10.1021/acscentsci.0c00988
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author Ongari, Daniele
Talirz, Leopold
Smit, Berend
author_facet Ongari, Daniele
Talirz, Leopold
Smit, Berend
author_sort Ongari, Daniele
collection PubMed
description [Image: see text] Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials’ performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.
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spelling pubmed-77060982020-12-02 Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution Ongari, Daniele Talirz, Leopold Smit, Berend ACS Cent Sci [Image: see text] Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials’ performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications. American Chemical Society 2020-10-02 2020-11-25 /pmc/articles/PMC7706098/ /pubmed/33274268 http://dx.doi.org/10.1021/acscentsci.0c00988 Text en This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Ongari, Daniele
Talirz, Leopold
Smit, Berend
Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution
title Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution
title_full Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution
title_fullStr Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution
title_full_unstemmed Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution
title_short Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution
title_sort too many materials and too many applications: an experimental problem waiting for a computational solution
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706098/
https://www.ncbi.nlm.nih.gov/pubmed/33274268
http://dx.doi.org/10.1021/acscentsci.0c00988
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