超小孔MOF MIL-120(Al) CO₂吸着剤における機械学習ポテンシャルを用いた局所的骨格ダイナミクスの解読

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.