Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO<sub>2</sub>’s Chemisorption and Diffusion in Mg-MOF-74-Reference-Cited by-同舟云学术

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Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO₂’s Chemisorption and Diffusion in Mg-MOF-74

Published:2023-03-08 Issue:6 Volume:17 Page:5579-5587
ISSN:1936-0851
Container-title:ACS Nano
language:en
Short-container-title:ACS Nano

Author:

Zheng Bowen¹²,Oliveira Felipe Lopes³⁴,Neumann Barros Ferreira Rodrigo³^ORCID,Steiner Mathias³,Hamann Hendrik¹,Gu Grace X.²^ORCID,Luan Binquan¹^ORCID

Affiliation:

1. IBM Research, Yorktown Heights, New York 10598, United States

2. Department of Mechanical Engineering, University of California, Berkeley, California 94720, United States

3. IBM Research, Av. República do Chile, 330, CEP 20031-170 Rio de Janeiro, RJ, Brazil

4. Department of Organic Chemistry, Instituto de Química, Universidade Federal do Rio de Janeiro, CEP 21941-909 Rio de Janeiro, RJ, Brazil

Publisher

American Chemical Society (ACS)

Subject

General Physics and Astronomy,General Engineering,General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsnano.2c11102

Reference50 articles.

1. Global-scale temperature patterns and climate forcing over the past six centuries

2. Porous materials for carbon dioxide separations

3. Introduction to Metal–Organic Frameworks

4. Understanding the diversity of the metal-organic framework ecosystem

5. Carbon Dioxide Capture in Metal–Organic Frameworks

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