Machine-Learning Prediction of Metal-Organic Framework Guest Accessibility from Linker and Metal Chemistry

Petuya, Remi ORCID: 0000-0002-3118-6966, Durdy, Samantha, Antypov, Dmytro ORCID: 0000-0003-1893-7785, Gaultois, Michael W ORCID: 0000-0003-2172-2507, Berry, Neil G ORCID: 0000-0003-1928-0738, Darling, George R ORCID: 0000-0001-9329-9993, Katsoulidis, Alexandros P ORCID: 0000-0003-0860-7440, Dyer, Matthew S ORCID: 0000-0002-4923-3003 and Rosseinsky, Matthew J ORCID: 0000-0002-1910-2483 (2022) Machine-Learning Prediction of Metal-Organic Framework Guest Accessibility from Linker and Metal Chemistry. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 61 (9).

Access the full-text of this item by clicking on the Open Access link.

Item Type:	Article
Uncontrolled Keywords:	Database, Guest accessibility, Machine learning, Metal-organic frameworks, Porosity
Divisions:	Faculty of Science and Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	24 Jan 2022 10:52
Last Modified:	24 Jun 2022 00:10
DOI:	10.1002/anie.202114573
Open Access URL:	https://doi.org/10.1002/anie.202114573
Related URLs:	Author
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3147534