Deep learning-based recommendation system for metal–organic frameworks (MOFs)-Reference-Cited by-同舟云学术

Deep learning-based recommendation system for metal–organic frameworks (MOFs)

Published:2024 Issue:7 Volume:3 Page:1410-1420
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Zhang Xiaoqi¹^ORCID,Jablonka Kevin Maik¹²³^ORCID,Smit Berend¹^ORCID

Affiliation:

1. Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne(EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland

2. Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstrasse 10, 07743 Jena, Germany

3. Helmholtz Institute for Polymers in Energy Applications Jena (HIPOLE Jena), Lessingstrasse 12-14, 07743 Jena, Germany

Abstract

This work presents a recommendation system for metal–organic frameworks (MOFs) inspired by online content platforms.

Funder

Carl-Zeiss-Stiftung

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Grantham Foundation for the Protection of the Environment

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D4DD00116H

Reference72 articles.

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4. DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining

5. Practical MOF Nanoarchitectonics: New Strategies for Enhancing the Processability of MOFs for Practical Applications