Investigation of the Effect of Framework Flexibility on Adsorption in SIFSIX-3-Cu using a Machine-Learned Force Field

Conference · Thu Mar 26 00:00:00 EDT 2026

DOI:https://doi.org/10.2172/3024525· OSTI ID:3024525

Findley, John ^[1]; Roper, Laine ^[2]; Sholl, David ^[3]; Steckel, Janice ^[4]

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1. NETL Site Support Contractor, National Energy Technology Laboratory
2. Oak Ridge Institute for Science and Education (ORISE)
3. Oak Ridge National Laboratory (ORNL)
4. NETL

Metal-organic frameworks (MOFs) are a promising class of adsorbents. The performance of MOF sorbents relies on high selectivity and low regeneration energy. This work focuses on the use of machine learned force fields (MLFFs) to model adsorption in a flexible MOF, SIFSIX-3-Cu. A DeePMD-based MLFF was trained to reproduce DFT (PBE+D3) energies, forces, and stresses, using an iterative sampling scheme combining sampling based on molecular dynamics, Monte Carlo, and geometry optimization to capture both attractive and repulsive regions of the potential energy surface. Flexibility of the MOF was explicitly included in this model. Hybrid Monte Carlo/molecular dynamics (MC/MD) simulations using the MLFF predicted adsorption isotherms in good agreement with experimental data for a range of pressures (40 Pa – 104 Pa) in contrast to rigid models, which overpredict CO2 adsorption at low pressures. The improvement was the result of a description of the variability of fluorine-fluorine diagonal distances at adsorption sites. This detailed description of flexibility afforded by the MLFF resulted in more accurate predictions adsorption isotherms when compared to the experimentally measured values. These results underscore the importance of including framework flexibility when modeling adsorption phenomena in MOFs, particularly for low pressure applications and provide a robust procedure for training MLFF models for MOFs.

• Research Organization: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy and Carbon Management (FECM); USDOE Office of Fossil Energy and Carbon Management (FECM), Office of Carbon Management (FE-20)
• OSTI ID: 3024525
• Resource Type: Conference presentation
• Conference Information: Conference Name: ACS Spring 2026 Location: Atlanta, GA, United States Start Date: 3/22/2026 12:00:00 AM End Date: 3/26/2026 12:00:00 AM
• Country of Publication: United States
• Language: English

Related Subjects

Atomistic Simulation
machine learning
Metal-Organic Framework
molecular dynamics
Monte Carlo