Investigation of the Effect of Framework Flexibility on CO <sub>2</sub> Adsorption in SIFSIX-3-Cu Using a Machine-Learned Force Field

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). These results underscore the importance of including framework flexibility when modeling adsorption phenomena in MOFs, particularly for low-pressure applications.

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Publication Details

Journal
The Journal of Physical Chemistry C
Published
2025-10-09
DOI
https://doi.org/10.1021/acs.jpcc.5c05096
Citations
2
Primary Topic
Catalytic Processes in Materials Science
Type
article
Field-Weighted Citation Impact
1.04
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article

Investigation of the Effect of Framework Flexibility on CO 2 Adsorption in SIFSIX-3-Cu Using a Machine-Learned Force Field

John M. Findley, David S. Sholl, Janice A. Steckel, Laine Roper
2 citations
The Journal of Physical Chemistry C
Catalytic Processes in Materials Science
1.04
article

Investigation of the Effect of Framework Flexibility on CO 2 Adsorption in SIFSIX-3-Cu Using a Machine-Learned Force Field

John M. Findley, David S. Sholl, Janice A. Steckel, Laine Roper
article en
2 citations

Abstract

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). These results underscore the importance of including framework flexibility when modeling adsorption phenomena in MOFs, particularly for low-pressure applications.

The Journal of Physical Chemistry CVol. 129(42)
Oak Ridge Associated Universities (US), Defense Logistics Agency (US), Oak Ridge National Laboratory (US), Harvard University Press (US), National Energy Technology Laboratory (US), University of Tennessee at Knoxville (US)
Openalex Percentile: Top 22%
Catalytic Processes in Materials Science
1.04
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