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

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Metal-organic frameworks (MOFs) with ultra-small pores are promising for gas capture, particularly CO2.
• The local dynamics of MOF frameworks, including functional groups and nodes, significantly influence gas sorption properties.
• Understanding these dynamics is essential for designing efficient MOF-based capture systems.

Purpose of the Study:

• To investigate the local dynamics of bridging hydroxyl groups (μ2-OH) in the ultra-small pore MOF MIL-120(Al).
• To assess the impact of these dynamics on CO2 adsorption behavior using advanced computational methods.
• To establish the importance of local structural dynamics for accurate MOF performance prediction.

Main Methods:

• Density Functional Theory (DFT) calculations were employed.
• A purpose-trained machine-learning potential (MLP) was developed and utilized.
• Grand canonical Monte Carlo (GCMC) and GCMC-Molecular Dynamics (GCMC-MD) simulations were performed.

Main Results:

• Six distinct μ2-OH configurations with low interconversion barriers were identified, indicating significant room-temperature dynamics.
• Adsorption isotherms and low-pressure behavior were found to be sensitive to μ2-OH ordering and framework relaxation.
• MLP-driven GCMC-MD simulations accurately captured framework relaxation and dynamic μ2-OH reorientation under CO2 loading, unlike rigid force-field methods.

Conclusions:

• Local structural dynamics, specifically the reorientation of bridging hydroxyl groups, are critical for accurately describing guest molecule locations and energetics in ultra-small pore MOFs.
• Accurate modeling of MOF dynamics is essential for reliable predictions of CO2 capture performance.
• This study highlights the necessity of incorporating framework flexibility and local dynamics into simulations for advanced materials design.