Decoding local framework dynamics in the ultra-small pore MOF MIL-120(Al) CO₂ adsorbent using machine-learning potential.

Abstract

Metal-organic frameworks (MOFs) with ultra-small pores offer an optimal environment to effectively capture guest molecules such as CO₂. Subtle local dynamics of their frameworks, either throughout reorientation of functional groups grafted to the organic linkers or those present in their inorganic nodes, is expected to play a major role in their sorption behaviours. Herein, we investigated the local dynamics of bridging hydroxyl group (μ₂-OH) in the ultra-small pore MOF MIL-120(Al) using DFT combined with a purpose-trained machine-learning potential (MLP). Six distinct μ₂-OH configurations were identified with low interconversion barriers (0.07-0.19 eV), indicating significant dynamic behaviour at room temperature. Grand canonical Monte Carlo and hybrid GCMC-MD simulations driven by the MLP demonstrate that adsorption isotherms and low-pressure behaviour are sensitive to μ₂-OH ordering and whether framework and cell relaxation are considered. While standard rigid force-field simulations overestimated the heat of adsorption, MLP-driven GCMC-MD simulations successfully captured framework relaxation and dynamic μ₂-OH reorientation under CO₂ loading. This work establishes that a reliable description of the local structure, such as reorientation/flipping of bridging hydroxyl groups, is a key feature to gain an accurate description of the guest locations and energetics in ultra-small pore MOFs.