Investigation of the Effect of Framework Flexibility on CO₂ Adsorption in SIFSIX-3-Cu Using a Machine-Learned Force Field

Journal Article · Thu Oct 09 00:00:00 EDT 2025 · Journal of Physical Chemistry. C

DOI:https://doi.org/10.1021/acs.jpcc.5c05096· OSTI ID:2999569

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

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1. National Energy Technology Laboratory (NETL), Pittsburgh, PA (United States); NETL Support Contractor, Pittsburgh, PA (United States)
2. National Energy Technology Laboratory (NETL), Pittsburgh, PA (United States); Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States); Harvard Univ., Cambridge, MA (United States)
3. UT-Oak Ridge Innovation Institute (UT-ORII), Oak Ridge, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
4. National Energy Technology Laboratory (NETL), Pittsburgh, PA (United States)

Metal–organic frameworks (MOFs) offer promise as selective CO₂ sorbents, but successful MOF sorbent materials need high CO₂ binding affinity and selectivity for CO₂ over water. This work focuses on the use of machine-learned force fields (MLFFs) to model CO₂ adsorption in flexible MOFs, with a focus on SIFSIX-3-Cu, an anion-pillared MOF known for its high CO₂ affinity. A preliminary high-throughput screening of over 900 anion-pillared MOFs was performed using rigid UFF+DDEC6 force fields to predict zero-loading heats of adsorption for CO₂ and H₂O. SIFSIX-3-Cu was selected for further computational study due to its predicted CO₂ heat of adsorption and experimental relevance. A DeePMD-based MLFF was trained to reproduce DFT (PBE+D3) energies and forces, with an iterative sampling scheme combining molecular dynamics, geometry optimization, random geometric insertion, and NVT Monte Carlo-based configuration generation to capture both attractive and repulsive regions of the potential energy surface. Flexibility of the MOF was explicitly included, contrasting with previous models that approximated the MOF as rigid. Hybrid Monte Carlo/molecular dynamics (MC/MD) simulations with the MLFF produced CO₂ adsorption isotherms in good agreement with experimental data at direct air capture (DAC) pressures (e.g., 40 Pa), in contrast to previous overestimations of CO₂ sorption by models with rigid structures. Bond and angle histogram analysis showed that MOF flexibility increased the variance of fluorine–fluorine diagonal distances at adsorption sites, resulting in a lower predicted sorption for flexible, asymmetric SIFSIX-3-Cu pore geometries compared to the rigid, symmetric DFT-optimized SIFSIX-3-Cu pore geometry. A detailed description of flexibility afforded by the MLFF resulted in an accurately predicted CO₂ uptake (0.88 mmol/g) at low pressure (40 Pa) compared to the experimentally measured value (1.24 mmol/g). In conclusion, these results underscore the importance of including framework flexibility when modeling adsorption phenomena in MOFs, particularly for low-pressure applications.

• Research Organization: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Fossil Energy and Carbon Management (FECM)
• OSTI ID: 2999569
• Journal Information: Journal of Physical Chemistry. C, Journal Name: Journal of Physical Chemistry. C Journal Issue: 42 Vol. 129; ISSN 1932-7455; ISSN 1932-7447
• Publisher: American Chemical Society
• Country of Publication: United States
• Language: English

Related Subjects

adsorption
machine learning potential
nanoporous metal organic frameworks