Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches-Reference-Cited by-同舟云学术

Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches

Published:2021 Issue:39 Volume:9 Page:13584-13599
ISSN:2050-7526
Container-title:Journal of Materials Chemistry C
language:en
Short-container-title:J. Mater. Chem. C

Author:

Zanca Federica¹,Glasby Lawson T.¹,Chong Sanggyu²^ORCID,Chen Siyu³^ORCID,Kim Jihan²,Fairen-Jimenez David⁴^ORCID,Monserrat Bartomeu³⁵,Moghadam Peyman Z.¹^ORCID

Affiliation:

1. Department of Chemical and Biological Engineering, The University of Sheffield, Sheffield S13JD, UK

2. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea

3. Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, UK

4. The Adsorption & Advanced Materials Laboratory (AAML), Department of Chemical Engineering & Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK

5. Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, UK

Abstract

Computational approaches accelerate design and discovery of conductive metal–organic frameworks (MOFs).

Funder

Engineering and Physical Sciences Research Council

University of Sheffield

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/TC/D1TC02543K