The application of machine learning for predicting the methane uptake and working capacity of MOFs-Reference-Cited by-同舟云学术

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

Published:2021 Issue: Volume:231 Page:224-234
ISSN:1359-6640
Container-title:Faraday Discussions
language:en
Short-container-title:Faraday Discuss.

Author:

Suyetin Mikhail¹^ORCID

Affiliation:

1. Institute of Nanotechnology, Karlsruhe Institute of Technology, P. O. Box 3640, 76021 Karlsruhe, Germany

Abstract

Multiple linear regression as a part of machine learning is employed to develop equations to predict the methane uptake and working capacity of MOFs. Only three geometrical descriptors are used in the equations: surface area, pore volume and density.

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/FD/D1FD00011J

Reference61 articles.

1. High-Pressure Methane Storage in Porous Materials: Are Carbon Materials in the Pole Position?

2. Analysis of High and Selective Uptake of CO2in an Oxamide-Containing {Cu2(OOCR)4}-Based Metal-Organic Framework

3. High-Capacity Methane Storage in Metal−Organic Frameworks M2(dhtp): The Important Role of Open Metal Sites

4. Fine Tuning of MOF-505 Analogues To Reduce Low-Pressure Methane Uptake and Enhance Methane Working Capacity

5. A partially interpenetrated metal–organic framework for selective hysteretic sorption of carbon dioxide

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