Computacional modeling of chirality in porous frameworks: Advanced methods and applications in enantioselective catalysis and separation

Chiral metal-organic frameworks (CMOFs) and covalent organic frameworks (CCOFs) have emerged as a class of crystalline porous materials with exceptional potential for applications demanding molecular-level stereocontrol, such as enantioselective separations and asymmetric catalysis. The modular nature of these frameworks allows for the rational design of chiral environments within their pores, yet the vastness of the accessible chemical space makes purely experimental exploration infeasible. Computational modeling has become an indispensable tool for navigating this complexity, providing atomic-scale insights into the mechanisms of chiral recognition that are often inaccessible to experimental probes [3]. This review provides a comprehensive and critical examination of the multiscale computational methodologies employed to study chirality in these porous frameworks. We survey the theoretical toolkit, from classical molecular mechanics (MM) force fields, essential for capturing framework dynamics and large-scale screening, to high-level quantum mechanical (QM) methods that are critical for elucidating the electronic structure and reaction pathways in asymmetric catalysis. We place particular emphasis on advanced techniques, including the development of flexible and machine learning force fields to overcome the limitations of generic models, the application of hybrid quantum mechanics/molecular mechanics (QM/MM) methods to balance accuracy and scale in complex catalytic systems, and the use of sophisticated Monte Carlo (GCMC) and molecular dynamics (MD) simulations to predict the thermodynamics and kinetics of enantioseparation. Through a detailed analysis of key case studies, we illustrate how these computational approaches have unraveled the principles of enantioselectivity, from the subtle interplay of non-covalent interactions in catalyst pockets to the role of pore geometry in sieving enantiomers. Finally, we explore frontier topics, including the modeling of structural defects and the transformative potential of machine learning (ML) in accelerating materials discovery. This review charts the progress of the field and outlines the grand challenges and future directions towards the predictive, rational design of chiral functional materials.