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1. High-throughput screening of hypothetical metal-organic frameworks for thermal conductivity

   https://doi.org/10.1038/s41524-022-00961-x  

   Islamov, Meiirbek ; Babaei, Hasan ; Anderson, Ryther ; Sezginel, Kutay B. ; Long, Jeffrey R. ; McGaughey, Alan J. H. ; Gomez-Gualdron, Diego A. ; Wilmer, Christopher E. ( January 2023 , npj Computational Materials)

   Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivitykin MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm⁻³), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m⁻¹ K⁻¹), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (> 10 W m⁻¹ K⁻¹), the structures of which we describe in additional detail.

    

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2. Active learning for efficient navigation of multi-component gas adsorption landscapes in a MOF

   https://doi.org/10.1039/D3DD00106G  

   Mukherjee, Krishnendu ; Osaro, Etinosa ; Colón, Yamil J. ( October 2023 , Digital Discovery)

   In recent decades, metal–organic frameworks (MOFs) have gained recognition for their potential in multicomponent gas separations. Though molecular simulations have revealed structure–property relationships of MOF–adsorbate systems, they can be computationally expensive and there is a need for surrogate models that can predict the adsorption data faster. In this work, an active learning (AL) protocol is introduced that can predict multicomponent gas adsorption in a MOF for a range of thermodynamic conditions. This methodology is applied to build a model for the adsorption of three different gas mixtures (CO2–CH4, Xe–Kr, and H2S–CO2) in the MOF Cu-BTC. A Gaussian process regression (GPR) model is used to fit the data as well to leverage its predicted uncertainty to drive the learning. The training data is generated using grand-canonical Monte Carlo (GCMC) simulations as points are iteratively added to the model to minimize the predicted uncertainty. Also, a criteria which captures the perceived performance of the GPs is introduced to terminate the AL process when the perceived accuracy threshold is met. The three systems are tested for a pressure–mole fraction (P–X), and a pressure–mole fraction–temperature (P–X–T) feature space. It is demonstrated that AL one only needs a fraction of the data from simulations to build a reliable surrogate model for predicting mixture adsorption. Further, the final GP fit from AL outperforms ideal adsorbed solution theory predictions. 

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3. Elucidating correlated defects in metal organic frameworks using theory-guided inelastic neutron scattering spectroscopy

   https://doi.org/10.1039/D2MH00914E  

   Cavalcante, Lucas S. ; Dettmann, Makena A. ; Sours, Tyler ; Yang, Dong ; Daemen, Luke L. ; Gates, Bruce C. ; Kulkarni, Ambarish R. ; Moulé, Adam J. ( January 2023 , Materials Horizons)

   Metal organic frameworks (MOFs) that incorporate metal oxide cluster nodes, exemplified by UiO-66, have been widely studied, especially in terms of their deviations from the ideal, defect-free crystalline structures. Although defects such as missing linkers, missing nodes, and the presence of adventitious synthesis-derived node ligands (such as acetates and formates) have been proposed, their exact structures remain unknown. Previously, it was demonstrated that defects are correlated and span multiple unit cells. The highly specialized techniques used in these studies are not easily applicable to other MOFs. Thus, there is a need to develop new experimental and computational approaches to understand the structure and properties of defects in a wider variety of MOFs. Here, we show how low-frequency phonon modes measured by inelastic neutron scattering (INS) spectroscopy can be combined with density functional theory (DFT) simulations to provide unprecedented insights into the defect structure of UiO-66. We are able to identify and assign peaks in the fingerprint region (<100 cm −1 ) which correspond to phonon modes only present in certain defective topologies. Specifically, this analysis suggests that our sample of UiO-66 consists of predominantly defect-free fcu regions with smaller domains corresponding to a defective bcu topology with 4 and 2 acetate ligands bound to the Zr 6 O 8 nodes. Importantly, the INS/DFT approach provides detailed structural insights ( e.g. , relative positions and numbers of acetate ligands) that are not accessible with microscopy-based techniques. The quantitative agreement between DFT simulations and the experimental INS spectrum combined with the relative simplicity of sample preparation, suggests that this methodology may become part of the standard and preferred protocol for the characterization of MOFs, and, in particular, for elucidating the structure defects in these materials. 

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4. Sequential design of adsorption simulations in metal–organic frameworks

   https://doi.org/10.1039/D1ME00138H  

   Mukherjee, Krishnendu ; Dowling, Alexander W. ; Colón, Yamil J. ( December 2021 , Molecular Systems Design & Engineering)

   The large number of possible structures of metal–organic frameworks (MOFs) and their limitless potential applications have motivated molecular modelers and researchers to develop methods and models to efficiently assess MOF performance. Some of the techniques include large-scale high-throughput molecular simulations and machine learning models. Despite those advances, the number of possible materials and the potential conditions that could be used still pose a formidable challenge for model development requiring large data sets. Therefore, there is a clear need for algorithms that can efficiently explore the spaces while balancing the number of simulations with prediction accuracy. Here, we present how active learning can sequentially select simulation conditions for gas adsorption, ultimately resulting in accurate adsorption predictions with an order of magnitude lower number of simulations. We model adsorption of pure components methane and carbon dioxide in Cu–BTC. We employ Gaussian process regression (GPR) and use the resulting uncertainties in the predictions to guide the next sampling point for molecular simulation. We outline the procedure and demonstrate how this model can emulate adsorption isotherms at 300 K from 10 −6 to 300 bar (methane)/100 bar (carbon dioxide). We also show how this procedure can be used for predicting adsorption on a temperature–pressure phase space for a temperature range of 100 to 300 K, and pressure range of 10 −6 to 300 bar (methane)/100 bar (carbon dioxide). 

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5. Heterometallic Actinide‐Containing Photoresponsive Metal‐Organic Frameworks: Dynamic and Static Tuning of Electronic Properties

   https://doi.org/10.1002/anie.202016826  

   Martin, Corey R. ; Leith, Gabrielle A. ; Kittikhunnatham, Preecha ; Park, Kyoung Chul ; Ejegbavwo, Otega A. ; Mathur, Abhijai ; Callahan, Cameron R. ; Desmond, Shelby L. ; Keener, Myles R. ; Ahmed, Fiaz ; et al ( February 2021 , Angewandte Chemie International Edition)

   Acquiring fundamental knowledge of properties of actinide‐based materials is a necessary step to create new possibilities for addressing the current challenges in the nuclear energy and nuclear waste sectors. In this report, we established a photophysics–electronics correlation for actinide‐containing metal‐organic frameworks (An‐MOFs) as a function of excitation wavelength, for the first time. A stepwise approach for dynamically modulating electronic properties was applied for the first time towards actinide‐based heterometallic MOFs through integration of photochromic linkers. Optical cycling, modeling of density of states near the Fermi edge, conductivity measurements, and photoisomerization kinetics were employed to shed light on the process of tailoring optoelectronic properties of An‐MOFs. Furthermore, the first photochromic MOF‐based field‐effect transistor, in which the field‐effect response could be changed through light exposure, was constructed. As a demonstration, the change in current upon light exposure was sufficient to operate a two‐LED fail‐safe indicator circuit.

    

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