scholarly journals Diversifying databases of metal organic frameworks for high-throughput computational screening

2021 ◽  
Author(s):  
Sauradeep Majumdar ◽  
Seyed Mohamad Moosavi ◽  
Kevin Maik Jablonka ◽  
Daniele Ongari ◽  
Berend Smit
Keyword(s):  
Hydrogen Storage ◽  
Carbon Capture ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Zeolite 13X ◽  
Computational Screening ◽  
Learning Techniques ◽  
Metal Organic ◽  
Grand Canonical ◽  
Better Than

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical MOFs, we need to assure that they not only contribute towards the growth of the count of structures but also add different chemistry to existing databases. In this study, we designed a database of ~20,000 hypothetical MOFs which are diverse in terms of their chemical design space—metal nodes, organic linkers, functional groups and pore geometries. Using Machine Learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications—post combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post combustion carbon capture) and MOF-5 (for hydrogen storage).

scholarly journals Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

2020 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Impedance Measurements ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

scholarly journals Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

2021 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Impedance Measurements ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

scholarly journals Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units

2021 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Secondary Building Units ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

Molecular Modeling Studies on a series of Metal-Organic Frameworks

2004 ◽  
Vol 837 ◽  
Author(s):  
Tae-Bum Lee ◽  
Daejin Kim ◽  
Seung-Hoon Choi ◽  
Eungsung Lee ◽  
Youjin Oh ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Hydrogen Storage ◽  
Density Functional ◽  
Storage Capacity ◽  
Metal Organic Frameworks ◽  
Adsorbed Hydrogen ◽  
Grand Canonical Monte Carlo ◽  
Hydrogen Storage Capacity ◽  
Metal Organic ◽  
Grand Canonical

ABSTRACTIn order to explore rational designs and synthetic strategies toward efficient hydrogen storage materials, quantum mechanical calculations and grand canonical Monte Carlo simulations have been carried out on a series of the Metal-Organic Frameworks containing various organic linkers. The calculations for specific surface areas and the shape of frontier orbitals for various frameworks indicate that the hydrogen storage capacity is largely dependent on the effective surface area of the material, rather than the free volume. Based on the iso-electrostatic potential surface from density functional calculations and the theoretical amount of adsorbed hydrogen from the grand canonical Monte Carlo calculations, it was also found that the electron localization around the organic linker plays an important role in the hydrogen storage capacity of Metal-Organic Frameworks. The prediction of the modeling study is supported by the hydrogen adsorption experiments with IRMOF-1 and -3, revealing the more enhanced hydrogen storage capacity of IRMOF-3 compared with that of IRMOF-1 at 77 K and H2 1 atm.

scholarly journals Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units

2021 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Secondary Building Units ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

scholarly journals Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

2020 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Impedance Measurements ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

Tuning the Redox Activity of Metal−Organic Frameworks for Enhanced, Selective O2 Binding

2019 ◽  
Author(s):  
Andrew Rosen ◽  
M. Rasel Mian ◽  
Timur Islamoglu ◽  
Haoyuan Chen ◽  
Omar Farha ◽  
...  
Keyword(s):  
Density Functional ◽  
Metal Organic Frameworks ◽  
Redox Activity ◽  
Screening Methods ◽  
Bridging Ligands ◽  
Metal Centers ◽  
Computational Screening ◽  
Open Metal Sites ◽  
Metal Organic ◽  
Unsaturated Metal Sites

<p>Metal−organic frameworks (MOFs) with coordinatively unsaturated metal sites are appealing as adsorbent materials due to their tunable functionality and ability to selectively bind small molecules. Through the use of computational screening methods based on periodic density functional theory, we investigate O<sub>2</sub> and N<sub>2</sub> adsorption at the coordinatively unsaturated metal sites of several MOF families. A variety of design handles are identified that can be used to modify the redox activity of the metal centers, including changing the functionalization of the linkers (replacing oxido donors with sulfido donors), anion exchange of bridging ligands (considering μ-Br<sup>-</sup>, μ-Cl<sup>-</sup>, μ-F<sup>-</sup>, μ-SH<sup>-</sup>, or μ-OH<sup>-</sup> groups), and altering the formal oxidation state of the metal. As a result, we show that it is possible to tune the O<sub>2</sub> affinity at the open metal sites of MOFs for applications involving the strong and/or selective binding of O<sub>2</sub>. In contrast with O<sub>2</sub> adsorption, N<sub>2</sub> adsorption at open metal sites is predicted to be relatively weak across the MOF dataset, with the exception of MOFs containing synthetically elusive V<sup>2+</sup> open metal sites. As one example from the screening study, we predict that exchanging the μ-Cl<sup>-</sup> ligands of M<sub>2</sub>Cl<sub>2</sub>(BBTA) (H<sub>2</sub>BBTA = 1<i>H</i>,5<i>H</i>-benzo(1,2-d:4,5-d′)bistriazole) with μ-OH<sup>-</sup> groups would significantly enhance the strength of O<sub>2</sub> adsorption at the open metal sites without a corresponding increase in the N<sub>2</sub> affinity. Experimental investigation of Co<sub>2</sub>Cl<sub>2</sub>(BBTA) and Co<sub>2</sub>(OH)<sub>2</sub>(BBTA) confirms that the former exhibits only weak physisorption, whereas the latter is capable of chemisorbing O<sub>2</sub> at room temperature. The chemisorption behavior is attributed to the greater electron-donating character of the μ-OH<sup>-</sup><sub> </sub>ligands and the presence of H-bonding interactions between the μ-OH<sup>-</sup> bridging ligands and the O<sub>2</sub> adsorbate.</p>

Green Synthesis and Enhanced CO2 Capture Performance of Perfluorinated Cerium-Based Metal-Organic Frameworks with UiO-66 and MIL-140 Topology

2018 ◽  
Author(s):  
Roberto D’Amato ◽  
Anna Donnadio ◽  
Mariolino Carta ◽  
Claudio Sangregorio ◽  
Riccardo Vivani ◽  
...  
Keyword(s):  
Photoelectron Spectroscopy ◽  
Metal Organic Frameworks ◽  
Isosteric Heat ◽  
Experimental Conditions ◽  
Cerium Ammonium Nitrate ◽  
Ideal Adsorbed Solution Theory ◽  
Metal Organic ◽  
Adsorbed Solution ◽  
Better Than ◽  
The Ideal

Reaction of cerium ammonium nitrate and tetrafluoroterephthalic acid in water afforded two new metal-organic frameworks with UiO-66 [F4_UiO-66(Ce)] and MIL-140 [F4_MIL-140A(Ce)] topologies. The two compounds can be obtained in the same experimental conditions, just by varying the amount of acetic acid used as crystallization modulator in the synthesis. Both F4_UiO-66(Ce) and F4_MIL-140A(Ce) feature pores with size < 8 Å, which classifies them as ultramicroporous. Combination of X-ray photoelectron spectroscopy and magnetic susceptibility measurements revealed that both compounds contain a small amount of Ce(III), which is preferentially accumulated near the surface of the crystallites. The CO<sub>2</sub> sorption properties of F4_UiO-66(Ce) and F4_MIL-140A(Ce) were investigated, finding that they perform better than their Zr-based analogues. F4_MIL-140A(Ce) displays an unusual S-shaped isotherm with steep uptake increase at pressure < 0.2 bar at 298 K. This makes F4_MIL-140A(Ce) exceptionally selective for CO<sub>2</sub> over N<sub>2</sub>: the calculated selectivity, according to the ideal adsorbed solution theory for a 0.15:0.85 mixture at 1 bar and 293 K, is higher than 1900, amongst the highest ever reported for metal-organic frameworks. The calculated isosteric heat of CO<sub>2 </sub>adsorption is in the range of 38-40 kJ mol<sup>-1</sup>, indicating a strong physisorptive character.

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