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

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. This review emphasizes the critical role of molecular modeling in metal-organic framework (MOF) research. It explores the use of density functional theory, Monte Carlo, and molecular dynamics methods for obtaining atomistic-level insights into diverse MOF phenomena and properties. Additionally, it discusses machine learning techniques as valuable tools for enhancing accuracy and minimizing computational costs, serving as a productive supplement to conventional modeling methods.image

Automated summary

This article focuses on the role of atomic-level modeling in metal-organic framework (MOF) research, including key methods such as density functional theory, Monte Carlo simulations, and molecular dynamics simulations. These methods provide new insights into MOF properties, such as predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information for classical simulations. The use of machine learning techniques in quantum and classical simulations is also discussed, which can enhance accuracy, reduce computational costs, and optimize MOF stability.