Inverse design of metal–organic frameworks for direct air capture of CO<sub>2</sub><i>via</i> deep reinforcement learning-Reference-Cited by-同舟云学术

Inverse design of metal–organic frameworks for direct air capture of CO₂via deep reinforcement learning

Published:2024 Issue:4 Volume:3 Page:728-741
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Park Hyunsoo¹²^ORCID,Majumdar Sauradeep²^ORCID,Zhang Xiaoqi²^ORCID,Kim Jihan¹^ORCID,Smit Berend²^ORCID

Affiliation:

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

2. Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland

Abstract

A reinforcement learning framework enables the design and discovery of novel metal–organic frameworks (MOFs) for direct air capture of CO2 (DAC) in terms of CO2 heat of adsorption and CO2/H2O selectivity.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D4DD00010B

Reference80 articles.

1. Systematic Design of Pore Size and Functionality in Isoreticular MOFs and Their Application in Methane Storage

2. Data-driven design of metal–organic frameworks for wet flue gas CO2 capture

3. Computational Design of Metal–Organic Frameworks Based on Stable Zirconium Building Units for Storage and Delivery of Methane

4. Balancing gravimetric and volumetric hydrogen density in MOFs

5. Charge Separation and Charge Carrier Mobility in Photocatalytic Metal‐Organic Frameworks

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