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The application of machine learning for predicting the methane uptake and working capacity of MOFs

Suyetin, Mikhail 1
1 Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)

Abstract:

Multiple linear regression analysis, as a part of machine learning, is employed to develop equations for the quick and accurate prediction of the methane uptake and working capacity of metal–organic frameworks (MOFs). Only three crystal characteristics of MOFs (geometric descriptors) are employed for developing the equations: surface area, pore volume and density of the crystal structure. The values of the geometric descriptors can be obtained much more cheaply in terms of time and other resources compared to running calculations of gas sorption or performing experimental work. Within this work sets of equations are provided for the different cases studied: a series of MOFs with NbO topology, a set of benchmark MOFs with outstanding methane storage and working capacities, and the whole CoRE MOF database (11 000 structures).


Verlagsausgabe §
DOI: 10.5445/IR/1000135507
Veröffentlicht am 16.06.2022
Originalveröffentlichung
DOI: 10.1039/d1fd00011j
Scopus
Zitationen: 2
Web of Science
Zitationen: 2
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Nanotechnologie (INT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1359-6640, 1364-5498
KITopen-ID: 1000135507
HGF-Programm 43.31.01 (POF IV, LK 01) Multifunctionality Molecular Design & Material Architecture
Erschienen in Faraday discussions
Verlag Royal Society of Chemistry (RSC)
Band 231
Seiten 224 - 234
Vorab online veröffentlicht am 09.04.2021
Nachgewiesen in Web of Science
Dimensions
Scopus
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