Theoretical Modeling of Coordination Materials

This chapter examines the applications of coordination materials, specifically metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), in gas storage and catalysis. Theoretical modeling at the molecular level, including electronic structure calculations, molecular dynamics simulations, and machine learning techniques, is emphasized. The discussion focuses on gas capture, with an emphasis on carbon dioxide and hydrogen, utilizing Density Functional Theory (DFT) and computational studies within MOFs and COFs. Computational strategies for enhancing hydrogen storage, from fundamental DFT principles to applications in coordination chemistry, are covered. The chapter explores molecular modeling and simulations for predicting gas adsorption, crystal structures, and hydrogen diffusion within these materials. Strategies for improving hydrogen storage, such as tailoring pore size and utilizing open metal sites, are detailed. The chapter concludes by highlighting the application of machine learning in coordination materials research, particularly in predicting synthesis conditions, stability, guest accessibility, and inverse material design. Additionally, the influence of solvents on molecular properties and simulations regarding trace content effects in coordination materials is briefly discussed. This concise overview underscores the significance of theoretical modeling, DFT, and computational studies in gas adsorption within MOFs and COFs, showcasing the intersection of simulations and machine learning in material research.