Effects of MOF linker rotation and functionalization on methane uptake and diffusion

Yue, Shuwen; Oh, Changhwan; Nandy, Aditya; Terrones, Gianmarco; Kulik, Heather J.

doi:10.1039/d2me00237j

Cited by 7 publications

(6 citation statements)

References 90 publications

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“…In this study, we investigate competing effects in the behavior of H 2 O and CO 2 adsorbed in CALF-20 using advanced molecular dynamics (MD) simulations carried out with data-driven many-body potential energy functions (PEFs) that accurately predict the properties of water − and carbon dioxide , across a wide range of thermodynamic conditions and mixture compositions. CALF-20 is modeled as a flexible framework that can accommodate potential structural changes, which may yield distinct thermodynamic and dynamic properties of guest molecules as concluded in several studies. − These simulations allow us to precisely disentangle the competitive adsorption between H 2 O and CO 2 and provide insights at the molecular level, which can inform the design of MOFs specifically optimized for carbon capture applications.…”

Section: Introductionmentioning

confidence: 99%

Elucidating the Competitive Adsorption of H₂O and CO₂ in CALF-20: New Insights for Enhanced Carbon Capture Metal–Organic Frameworks

Ho,

Paesani

2023

ACS Appl. Mater. Interfaces

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In light of the pressing need for efficient carbon capture solutions, our study investigates the simultaneous adsorption of water (H 2 O) and carbon dioxide (CO 2 ) as a function of relative humidity in CALF-20, a highly scalable and stable metal− organic framework (MOF). Advanced computer simulations reveal that due to their similar interactions with the framework, H 2 O and CO 2 molecules compete for the same binding sites, occupying similar void regions within the CALF-20 pores. This competition results in distinct thermodynamic and dynamical behaviors of H 2 O and CO 2 molecules, depending on whether one or both guest species are present. Notably, the presence of CO 2 molecules forces the H 2 O molecules to form more connected hydrogen-bond networks within smaller regions, slowing water reorientation dynamics and decreasing water entropy. Conversely, the presence of water speeds up the reorientation of CO 2 molecules, decreases the CO 2 entropy, and increases the propensity for CO 2 to be adsorbed within the framework due to stronger water-mediated interactions. Due to the competition for the same void spaces, both H 2 O and CO 2 molecules exhibit slower diffusion when molecules of the other guest species are present. These findings offer valuable strategies and insights into enhancing the differential affinity of H 2 O and CO 2 for MOFs specifically designed for carbon capture applications.

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Section: Introductionmentioning

confidence: 99%

Elucidating the Competitive Adsorption of H₂O and CO₂ in CALF-20: New Insights for Enhanced Carbon Capture Metal–Organic Frameworks

Ho,

Paesani

2023

ACS Appl. Mater. Interfaces

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“…These ghost atoms act as positioning vectors directing the attachment of the selected groups on the organic core structure. The algorithm used to add these chemical groups is similar to that proposed by Yue et al, however, with some notable differences. First, these chemical groups are added in a way that maintains the point group presented by the organic core for the specific geometry of the building block.…”

Section: Resultsmentioning

confidence: 99%

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

Oliveira,

Esteves

2024

J. Chem. Inf. Model.

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Covalent organic frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry, making them an ideal choice for a wide range of applications. However, exploring COFs experimentally can be arduous and time-consuming due to their immense number of potential structures. As a result, computational high-throughput studies have become an attractive option. Nevertheless, generating COF structures can also be a challenging and time-consuming task. To address this challenge, here, we introduce the pyCOFBuilder, an open-source Python package designed to facilitate the generation of COF structures for computational studies. The pyCOFBuilder software provides an easy-to-use set of functionalities to generate COF structures following the reticular approach. In this paper, we describe the implementation, main features, and capabilities of the pyCOFBuilder, demonstrating its utility for generating COF structures with varying topologies and chemical properties. pyCOFBuilder is freely available on GitHub at https://github.com/ lipelopesoliveira/pyCOFBuilder.

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“…However, we generated a CIF for the latter MOF by replacing hydrogen atoms in the CAU-10-H CIF with the functional group NO 2 using molSimplify functionality. 124 Through these steps taken to locate CIFs and prepare them for analysis, we were able to collect adequate CIFs for 181 of the 207 MOFs classified by Burtch et al 41 For the remaining 26 MOFs, no associated CIF structure could be found or the CIF had too much disorder or unusual bond lengths to readily use for featurization. We required CIFs in order to generate ML-ready features that depend on graph connectivity and geometric information of the MOFs.…”

Section: Computational Detailsmentioning

confidence: 99%

“…Where necessary, we removed disordered atoms from CIFs manually and also added missing hydrogen atoms using Mercury 2021.1.0 . Overlapping atoms and floating solvent were removed and functional groups were added using molSimplify 1.7.3. − Metal identity modification and functional group addition were necessary in cases where we could not find a CIF for a MOF but could find a CIF for a similar MOF. To generate structures by changing metals in CIFs, we also used Mercury or manual replacement at the command line.…”

Section: Computational Detailsmentioning

confidence: 99%

Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set

Terrones,

Huang,

Rivera

et al. 2024

J. Am. Chem. Soc.

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Metal−organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity of water. Consequently, it is useful to predict whether a MOF is water-stable before investing time and resources into synthesis. Existing heuristics for designing water-stable MOFs lack generality and limit the diversity of explored chemistry due to narrowly defined criteria. Machine learning (ML) models offer the promise to improve the generality of predictions but require data. In an improvement on previous efforts, we enlarge the available training data for MOF water stability prediction by over 400%, adding 911 MOFs with water stability labels assigned through semiautomated manuscript analysis to curate the new data set WS24. The additional data are shown to improve ML model performance (test ROC-AUC > 0.8) over diverse chemistry for the prediction of both water stability and stability in harsher acidic conditions. We illustrate how the expanded data set and models can be used with a previously developed activation stability model in combination with genetic algorithms to quickly screen ∼10,000 MOFs from a space of hundreds of thousands for candidates with multivariate stability (upon activation, in water, and in acid). We uncover metal-and geometry-specific design rules for robust MOFs. The data set and ML models developed in this work, which we disseminate through an easy-to-use web interface, are expected to contribute toward the accelerated discovery of novel, water-stable MOFs for applications such as direct air gas capture and water treatment.

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Effects of MOF linker rotation and functionalization on methane uptake and diffusion

Abstract: The flexible degrees of freedom in metal–organic frameworks (MOFs) can have significant effects on guest molecule behavior. However, in the majority of studies applying molecular simulations to MOFs, the framework...

Cited by 7 publications

References 90 publications

Elucidating the Competitive Adsorption of H₂O and CO₂ in CALF-20: New Insights for Enhanced Carbon Capture Metal–Organic Frameworks

Elucidating the Competitive Adsorption of H₂O and CO₂ in CALF-20: New Insights for Enhanced Carbon Capture Metal–Organic Frameworks

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set

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Effects of MOF linker rotation and functionalization on methane uptake and diffusion

Abstract: The flexible degrees of freedom in metal–organic frameworks (MOFs) can have significant effects on guest molecule behavior. However, in the majority of studies applying molecular simulations to MOFs, the framework...

Cited by 7 publications

References 90 publications

Elucidating the Competitive Adsorption of H2O and CO2 in CALF-20: New Insights for Enhanced Carbon Capture Metal–Organic Frameworks

Elucidating the Competitive Adsorption of H2O and CO2 in CALF-20: New Insights for Enhanced Carbon Capture Metal–Organic Frameworks

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set

Contact Info

Product

Resources

About

Elucidating the Competitive Adsorption of H₂O and CO₂ in CALF-20: New Insights for Enhanced Carbon Capture Metal–Organic Frameworks

Elucidating the Competitive Adsorption of H₂O and CO₂ in CALF-20: New Insights for Enhanced Carbon Capture Metal–Organic Frameworks