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Exploring the Structural, Dynamic, and Functional Properties of Metal–Organic Frameworks through Molecular Modeling

Journal Article · · Advanced Functional Materials
 [1];  [2];  [3];  [3];  [3]
  1. Northwestern University, Evanston, IL (United States); Wroclaw University of Science and Technology (Poland)
  2. Northwestern University, Evanston, IL (United States); University at Buffalo, The State University of New York, NY (United States)
  3. Northwestern University, Evanston, IL (United States)

This review spotlights the role of atomic-level modeling in research on metal-organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster-based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high-throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.

Research Organization:
University of Minnesota, Minneapolis, MN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); U.S. National Science Foundation (NSF)
Grant/Contract Number:
SC0023454
OSTI ID:
2510792
Journal Information:
Advanced Functional Materials, Journal Name: Advanced Functional Materials Journal Issue: 43 Vol. 34; ISSN 1616-301X
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

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