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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Flexible MOFs as Pressure-Tunable Filters for Hydrocarbon Separation

Metal–organic frameworks (MOFs) hold significant promise for separating gas mixtures, especially hydrocarbons. While the main focus in the field is the development of new adsorbents for specific separations of binary mixtures, a preferable situation would be using a single framework for separating various species. This has been achieved in some flexible MOFs, where ternary mixtures can be separated at different temperatures. Here, we propose a simple yet fascinating way of utilizing the flexibility of MOFs to create tunable filters as a function of external pressure. We thus replace the more costly temperature-driven tunability with a cost-efficient external-pressure tunability. As a proof-of-concept, we select the CaMOF flexible framework for C6 hydrocarbon separation, however, our results are applicable to other flexible framework and gas molecules. Furthermore, our findings provide mechanistic insight and guidelines to engineer separation filters by designing flexible pores with critical sizes that can be effectively manipulated by external pressure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uniting Theory and Experiment to Deliver Flexible MOFS for Superior Methane (NG) Storage

The objective of the project was to use previous insights developed through synthesis and quantitative modeling of rigid metal–organic frameworks (MOFs) in an established synergistic theoretical/experimental team to create, modify, and evaluate flexible MOFs (FlexMOFs) for natural gas (NG) storage and release at practically useful pressures and transform the NG storage economy. The goal was reduced pressure absorbed natural gas (ANG) FlexMOF storage at operating pressures less than 100 bar with physisorption exploiting the favorable thermodynamics and kinetics of flexible porous material opening in response to adsorption. Hydrogen behavior in this context was also considered. The specific aim of the project was to design and develop a standard computational modeling methodology for, first, detailed atomistic retrodiction of FlexMOF gating behavior and ultimately prediction of the effects of functionalization and/or substitution on structural transitioning. The project was also geared towards the establishment of the interaction of methane with the framework and binding sites using modeling and the FlexMOF will be both internally and external validated as a SMART metric. This has value to the scientific community both from the obvious standpoint of providing a better understanding of the promising test case systems (CdIF-13 and the MIL-53(Al) series of MOFs), but also in providing an avenue of obtaining insight into these FlexMOF systems in general, which is of particular interest given the tendency of structure-function correlation to lag behind synthesis methodology (making the latter a hit or miss proposition for applications). This gap remains significant for FlexMOF systems whose gate opening behaviors complicate computational examination. The computational methodologies are relatively inexpensive in terms of both money and computational resources enhancing the general viability of these methodologies. The ultimate gain to the public will be in the application of these techniques to design systems for natural gas storage and use to cut down on green-house emissions.

03 NATURAL GAS↗
DOE OSTI2024

Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

To cope with legacy greenhouse gas emissions and to achieve net-zero emissions by 2050, the U.S. Department of Energy (DOE) is funding efforts to develop direct air capture (DAC), a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been well studied as DAC sorbent materials due to their tunable structural and compositional properties. Thermodynamic simulations using force fields are often used to provide predictions of a material’s performance in many separations. However, these force fields often make assumptions about bonds and the physics of the adsorption process. A new class of force fields called machine-learned force fields (MLFFs) use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). In this work, models were developed to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption using MLFFs. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption in several MOFs.

Findley, John↗
DOE OSTI2024

Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

To cope with legacy greenhouse gas emissions and to achieve net-zero emissions by 2050, the U.S. Department of Energy (DOE) is funding efforts to develop direct air capture (DAC), a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been studied as DAC sorbent materials because of their structural and chemical diversity. Thermodynamic calculations using classical force fields are often used to screen MOFs for their performance in separations such as CO2 capture. Machine-learned force fields (MLFFs) can use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). These descriptions of forces and energies can be used to improve the accuracy of adsorption calculations. In this work, MLFF models were developed for MOFs to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption properties.

Findley, John↗
DOE OSTI2024

Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

Direct air capture (DAC) is a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been studied as DAC sorbent materials because of their structural and chemical diversity. Thermodynamic calculations using classical force fields are often used to evaluate MOFs for their performance in separations such as CO2 capture. Machine-learned force fields (MLFFs) can use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). These descriptions of forces and energies can be used to improve the accuracy of adsorption calculations. In this work, classical models were used to pre-screen MOFs for CO2 capture. DFT calculations were then used to examine the adsorption mechanism. Next, MLFF models were developed for MOFs to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption properties.

Findley, John↗
DOE OSTI2024

Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

Metal organic frameworks (MOFs) are a large class of porous materials and have garnered significant interest due to their large surface areas and their tunable physical and chemical properties. Numerous prior studies have been performed to screen large databases of this material class for promising DAC sorbent materials. These studies have often relied on classical model potentials. While density functional theory (DFT) calculations have been shown to be very accurate for modeling the interaction of CO2 with MOFs, such calculations are too computationally demanding for statistically significant adsorption predictions. To overcome this barrier, we developed methods for training models to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption using machine learned force fields (MLFFs). These methods were parametrized based on DFT calculations of CO2 in a flexible MOF and used to predict MOF structural properties as well as CO2 adsorption in several MOFs.

Findley, John↗
DOE OSTI2025

Facilitating Screening of MOFs for Mixed Matrix Membranes Using Machine Learning and the Maxwell Model

Metal organic framework (MOF)-based mixedmatrix membranes (MMMs), which embed MOF particles in polymer matrices, combine the advantages of polymeric and inorganic membranes. Multiple previous studies have used the Maxwell model together with molecular simulations and machine learning (ML) to predict the performance of MOF/polymer MMMs. However, the assumption of rigid MOF frameworks in molecular simulations limited the accuracy of the data used in the predictions, particularly in predicting molecular diffusivities. We developed a novel workflow integrating ML models with consideration of MOF flexibility to predict the permeability and selectivity of 131,722 MMMs for CO 2 /CH 4 , O 2 /N 2 and He/H 2 separations. The full range of achievable MMM performance within the Maxwell model was analyzed, and several promising MOFs were identified using this workflow. This approach offers an efficient tool for screening any polymer and MOF combination in gas separation applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Kinetic Separation of Siloxanes in Metal–Organic Frameworks

We present an in silico assessment of metal–organic frameworks (MOFs) for the kinetic separation of linear and cyclic siloxanes. We employed molecular dynamics simulations investigating both rigid and flexible 1D MOF frameworks to identify a specific range of pore parameters that enables the diffusion of linear siloxanes but leads to slow diffusion of cyclic siloxanes. We then extended our analysis to flexible 3D MOFs to select adsorbents for the kinetic separation of cyclic and linear siloxanes. Based on synthesizability metrics we identified four 3D MOFs capable of discriminating between cyclic and linear siloxanes. One of the MOFs with structure code WIYFAM stood out with the ability to distinguish between cyclic and linear siloxanes and facilitate the diffusion of all linear siloxanes investigated in this study. One of the other MOFs, IRMOF-6, is found to be capable of not only discriminating between cyclic and linear siloxanes, but even between shorter and longer linear siloxanes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Harnessing nanoscale densification for controlling gas selectivity in flexible zeolitic imidazolate frameworks

Flexible metal-organic frameworks (MOFs) are promising for gas separation due to their molecular recognition capabilities. However, gate-opening behavior in certain flexible MOFs can reduce gas selectivity, enabling simultaneous adsorption of multiple gases. We introduce a binder-free, pressure-free densification process to enhance gas selectivity in flexible zeolitic imidazolate frameworks (ZIF-7 and ZIF-9) by optimizing nanoparticle packing. Unlike powders, the densified ZIF-7 and ZIF-9 retain rhombohedral crystal structures after solvent removal, confirmed via synchrotron powder X-ray diffraction. This process modifies adsorption behavior by introducing a diffusion barrier, enabling diffusivity-driven kinetic separation and enhanced CO2/hydrocarbon selectivity. At 0.5 bar, ZIF-7 mono shows 2×, 4×, and 9.5× higher CO 2 selectivity over C 2 H 2 , C 2 H 4 , and C 2 H 6 , respectively, versus ZIF-7 pwd . ZIF-9 mono similarly shows 6.5×, 3×, and 39× improvements over ZIF-9 pwd . Both monoliths outperform powders in ternary gas mixtures. This study provides a novel strategy to control gate opening and enhance gas selectivity in flexible MOFs for practical separation applications.

CO2/hydrocarbon separation↗
DOE OSTI2025

Combining Theory and Experiment to Map the Atomic-Level Structure–Energy Pathways of Adsorbate-Mediated Phase Changes in a Cooperatively Flexible Metal–Organic Framework

An important subclass of metal–organic frameworks (MOFs) exhibits cooperative flexibility, wherein individual crystallites undergo global structural phase changes in response to external stimuli. Where cooperative flexibility results in reversible changes between crystalline states of distinct accessible porosity, these frameworks can exhibit rare yet desirable behaviors that cannot be explained by local dynamics alone. Yet, the chemical and structural origins of cooperative flexibility and how frameworks undergo these reversible phase changes at the atomic level remain poorly understood. Deliberate design for specific applications is therefore exceedingly difficult, and there is great impetus to develop a fundamental understanding of this phenomenon. Here, an effective and widely accessible computational approach is developed, which is designed to provide microscopic resolution via direct comparison to experimental data along the desorption-guided pathway. The strategy is applied to explain the desorption-induced phase change in an experimentally well-characterized framework, CdIF-13 (sod-Cd(benzimidazolate)2), where experiment alone was unable to resolve the atomistically detailed phase change landscape. Our findings reveal that the cooperative phase change pathways are adsorbate dependent with thermodynamics of intermediate structural states dictated by a nuanced interplay of ligand orientation, skeletal symmetry, and modes of surface adsorption. The results reveal that this isotropically flexible framework is “chaperoned” through a complex energy landscape by specific adsorbates, revealed by the reported computational approach with atomic-level insight and validated by experimentally determined structures. Thus, this work facilitates both understanding and future design of flexible materials for applications in gas storage, transport, delivery, and separation technologies.

03 NATURAL GAS↗
DOE OSTI2025

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

Metal–organic frameworks (MOFs) offer promise as selective CO 2 sorbents, but successful MOF sorbent materials need high CO 2 binding affinity and selectivity for CO 2 over water. This work focuses on the use of machine-learned force fields (MLFFs) to model CO 2 adsorption in flexible MOFs, with a focus on SIFSIX-3-Cu, an anion-pillared MOF known for its high CO 2 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 CO 2 and H 2 O. SIFSIX-3-Cu was selected for further computational study due to its predicted CO 2 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 CO 2 adsorption isotherms in good agreement with experimental data at direct air capture (DAC) pressures (e.g., 40 Pa), in contrast to previous overestimations of CO 2 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 CO 2 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.

adsorption↗
DOE OSTI2026

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

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.

Atomistic Simulation↗
DOE OSTI2025

Comparing Classical and Machine Learning Force Fields for Modeling Deformation of Metal–Organic Frameworks Relevant for Direct Air Capture

Deformation of metal–organic frameworks (MOFs) induced by adsorbate molecules can affect adsorption properties such as capacity and selectivity, but most computational studies of MOFs assume framework rigidity to simplify calculations. Although flexible force fields (FFs) for MOFs have been parametrized for specific materials, the generality of FFs for reliably modeling adsorbate-induced deformation to accuracy nearing that of density functional theory (DFT) has not been established. This work confirms using DFT calculations that adsorbate-induced deformation can affect CO 2 and H 2 O adsorption energies in a considerable fraction of MOFs promising for direct air capture (DAC). We then benchmark the efficacy of several general-purpose FFs in describing adsorbate-induced deformation for DAC against DFT. Our results show that current classical FFs are insufficient for describing MOF deformation, especially in cases of interest for DAC where strong interactions exist between adsorbed molecules and MOF frameworks. Some emerging machine learning force fields (MLFFs) we tested, particularly CHGNet, MACE-MP-0, and Equiformer V2, appear to be more promising than the classical FF for emulating the deformation behavior described by DFT. The best performing FF (CHGNet), however, fails to achieve the accuracy required for practical predictions with a mean absolute adsorption energy error of 0.124 eV.

adsorption↗
DOE OSTI2026

Torsional Flexibility Tuning of Hexa-Carboxylate Ligands to Unlock Distinct Topological Access to Zirconium Metal–Organic Frameworks

Zirconium-based metal–organic frameworks (Zr-MOFs) exhibit remarkable structural diversity and functionality. However, uncovering new topological types within this family remains a considerable challenge today. Herein, we report two new hexa-topic ligands designed through the introduction of torsional flexibility, which enable the construction of two Zr-MOFs featuring rare network topologies. The (4,4′,4″,4‴,4‴′,4‴′′-((2-carboxybenzene-1,3,5-triyl)tris(9H-carbazole-9,3,6-triyl))hexabenzoic acid ligand (BTCH)) was obtained by replacing the rigid triptycene core in the H6PET-1 ligand (4,4′,4″,4‴,4‴′,4‴′′-(9,10-dihydro-9,10-[1,2]benzenoanthracene-2,3,6,7,14,15-hexayl)hexabenzoic acid) with a benzene-tricarbazole unit. Owing to its torsionally flexible core that allows rotational freedom to the arms, this ligand directs the construction of NU-2620 (NU represents Northwestern University), a Zr-MOF with 8-connected Zr6 clusters and the rare nuh topology. Further flexibilization of the carbazole units to benzene rings yielded the even more torsionally flexible 5′,5‴-bis(4-carboxyphenyl)-5″-(4,4″-dicarboxy-[1,1′:3′,1″-terphenyl]-5′-yl)-[1,1′:3′,1″:3″,1‴:3‴,1‴′-quinquephenyl]-4,4‴′-dicarboxylic acid ligand (CCTT), which forms NU-2630 featuring 6-connected clusters and the pcu topology. Both frameworks exhibit good chemical stability, prompting evaluation of their performance in CO2 photoreduction catalysis. Under low-concentration CO2 conditions, NU-2620 displays markedly higher catalytic activity than its benzene-based analogue, NU-2630, thanks to the abundance of photoactive carbazole units within its structure. These results demonstrate that introducing torsional flexibility in high-connected linkers can unlock access to new topologies and accelerates the reticular expansion of Zr-MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Machine Learning Interatomic Potentials for Modeling Framework Flexibility and Water Uptake in NbOFFIVE-1-Ni Metal–Organic Framework

Metal–organic frameworks (MOFs), with their distinctive porous structures and tunable chemical properties, have shown immense promise in the separation and storage of gases. Currently, the accurate simulation of their adsorptive properties remains challenging, especially for systems where the molecules fit very tightly into the pores. Traditional simulation methods often approximate the frameworks as rigid and do not account for the framework flexibility seen in materials such as NbOFFIVE-1-Ni. First-principles molecular dynamics (FPMD) simulations offer the desired accuracy in modeling this flexibility but are limited by their extensive computational demands, rendering them impractical for long simulations. Conversely, classical force field-based simulations offer computational efficiency but lack the necessary accuracy. Here, to break this accuracy-efficiency trade-off, we have developed machine learning interatomic potentials trained on energies and forces from FPMD to model the framework flexibility of NbOFFIVE-1-Ni in the presence of water over nanosecond time scales. Furthermore, by integrating MLIP-driven molecular dynamics (MLIP-MD) with grand canonical Monte Carlo (GCMC) simulations, we further incorporated framework flexibility into adsorption predictions, yielding water adsorption isotherms that better align with experimental data compared to those of conventional GCMC simulations. These advances offer new opportunities for the design and optimization of MOFs in gas storage and separation applications.

adsorption↗
DOE OSTI2025

Fabrication of Piezoelectric Polymer and Metal–Organic Framework Composite Thin Films Using Solution Shearing

Polymer-metal–organic framework (polymer-MOF) composites have garnered significant interest as polymers can enhance the processability and industrial applicability of MOFs. Thin films of these composites are particularly attractive for applications in sensing, separations, and flexible electronics. Solution shearing, a meniscus-guided coating technique, has emerged as a scalable process for fabricating thin films of MOFs, and can produce large-area films within minutes. In this study, we utilized solution shearing to fabricate composite thin films of a MOF UiO-66 and a piezoelectric polymer poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)), investigating how polymer concentration during MOF synthesis and composite formation influences thin film properties, including crystallinity, surface coverage, and piezoelectric performance. Additionally, solid-state NMR spectroscopy was utilized to probe the interactions between P(VDF-TrFE) and UiO-66 in the composite. Evidence from solid-state NMR indicated polymer-MOF interactions, suggesting that the polymer strands are in close proximity to the UiO-66 pores, supporting a mixed surface coating and pore infiltration model. Furthermore, incorporating P(VDF-TrFE) enhanced the film’s areal coverage from 70% to 100%. While the thermal conductivity remained essentially unchanged, the composite film showed an improved piezoelectric effect. The composite with 91 wt % P(VDF-TrFE) exhibited the highest output voltage of 9.1 V and a sensitivity of 0.26 V/N under applied pressure. This work demonstrates the potential of solution shearing as a scalable technique for fabricating polymer-MOF composite thin films.

P(VDF-TrFE)↗