ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning-Reference-Cited by-同舟云学术

ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning

Published:2023-01-20 Issue:3 Volume:35 Page:900-916
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Burner Jake¹^ORCID,Luo Jun¹,White Andrew¹,Mirmiran Adam¹,Kwon Ohmin¹,Boyd Peter G.¹,Maley Stephen¹,Gibaldi Marco¹^ORCID,Simrod Scott¹,Ogden Victoria¹,Woo Tom K.¹^ORCID

Affiliation:

1. Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie Private, OttawaK1N 6N5, Canada

Funder

Compute Canada

Total

Natural Sciences and Engineering Research Council of Canada

Mitacs

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.2c02485

Reference105 articles.

1. Metal–Organic Framework Materials with Ultrahigh Surface Areas: Is the Sky the Limit?

2. Catalysis by Metal Organic Frameworks: Perspective and Suggestions for Future Research

3. Magnetic ordering through itinerant ferromagnetism in a metal–organic framework

4. Ultrathin water-stable metal-organic framework membranes for ion separation

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