scholarly journals Machine Learning and High-Throughput Computational Screening of Hydrophobic Metal–Organic Frameworks for Capture of Formaldehyde from Air

2020 ◽  
Author(s):  
Xueying Yuan ◽  
Xiaomei Deng ◽  
Chengzhi Cai ◽  
Zenan Shi ◽  
Hong Liang ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Metal Organic Frameworks ◽  
Computational Screening ◽  
Metal Organic

Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks

2020 ◽  
Vol 5 (4) ◽  
pp. 725-742 ◽  
Author(s):  
Zenan Shi ◽  
Wenyuan Yang ◽  
Xiaomei Deng ◽  
Chengzhi Cai ◽  
Yaling Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
High Performance ◽  
Metal Organic Frameworks ◽  
Computational Screening ◽  
Metal Organic

The combination of machine learning and high-throughput computation for the screening of MOFs with high performance.

scholarly journals High-Throughput Screening of Metal–organic Frameworks for Macroscale Heteroepitaxial Alignment

2018 ◽  
Author(s):  
Andrew Tarzia ◽  
Masahide Takahashi ◽  
Paolo Falcaro ◽  
Aaron Thornton ◽  
Christian Doonan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Defect Formation ◽  
Metal Organic Frameworks ◽  
Rectangular Lattice ◽  
Copper Hydroxide ◽  
Computational Screening ◽  
Screening Algorithm ◽  
Wide Range ◽  
Metal Organic

The ability to align porous metal–organic frameworks (MOFs) on substrate surfaces on a macroscopic scale is a vital step towards integrating MOFs into functional devices. But macroscale surface alignment of MOF crystals has only been demonstrated in a few cases. To accelerate the materials discovery process, we have developed a high-throughput computational screening algorithm to identify MOFs that are likely to undergo macroscale aligned heterepitaxial growth on a substrate. Screening of thousands of MOF structures by this process can be achieved in a few days on a desktop workstation. The algorithm filters MOFs based on surface chemical compatibility, lattice matching with the substrate, and interfacial bonding. Our method uses a simple new computationally efficient measure of the interfacial energy that considers both bond and defect formation at the interface. Furthermore, we show that this novel descriptor is a better predictor of aligned heteroepitaxial growth than other established interface descriptors, by testing our screening algorithm on a sample set of copper MOFs that have been grown heteroepitaxially on a copper hydroxide surface. Application of the screening process to several MOF databases reveals that the top candidates for aligned growth on copper hydroxide comprise mostly MOFs with rectangular lattice symmetry in the plane of the substrate. This result indicates a substrate-directing effect that could be exploited in targeted synthetic strategies. We also identify that MOFs likely to form aligned heterostructures have broad distributions of in-plane pore sizes and anisotropies. Accordingly, this suggests that aligned MOF thin films with a wide range of properties may be experimentally accessible.

scholarly journals High-Throughput Screening of Metal–organic Frameworks for Macroscale Heteroepitaxial Alignment

2018 ◽  
Author(s):  
Andrew Tarzia ◽  
Masahide Takahashi ◽  
Paolo Falcaro ◽  
Aaron Thornton ◽  
Christian Doonan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Defect Formation ◽  
Metal Organic Frameworks ◽  
Rectangular Lattice ◽  
Copper Hydroxide ◽  
Computational Screening ◽  
Screening Algorithm ◽  
Wide Range ◽  
Metal Organic

The ability to align porous metal–organic frameworks (MOFs) on substrate surfaces on a macroscopic scale is a vital step towards integrating MOFs into functional devices. But macroscale surface alignment of MOF crystals has only been demonstrated in a few cases. To accelerate the materials discovery process, we have developed a high-throughput computational screening algorithm to identify MOFs that are likely to undergo macroscale aligned heterepitaxial growth on a substrate. Screening of thousands of MOF structures by this process can be achieved in a few days on a desktop workstation. The algorithm filters MOFs based on surface chemical compatibility, lattice matching with the substrate, and interfacial bonding. Our method uses a simple new computationally efficient measure of the interfacial energy that considers both bond and defect formation at the interface. Furthermore, we show that this novel descriptor is a better predictor of aligned heteroepitaxial growth than other established interface descriptors, by testing our screening algorithm on a sample set of copper MOFs that have been grown heteroepitaxially on a copper hydroxide surface. Application of the screening process to several MOF databases reveals that the top candidates for aligned growth on copper hydroxide comprise mostly MOFs with rectangular lattice symmetry in the plane of the substrate. This result indicates a substrate-directing effect that could be exploited in targeted synthetic strategies. We also identify that MOFs likely to form aligned heterostructures have broad distributions of in-plane pore sizes and anisotropies. Accordingly, this suggests that aligned MOF thin films with a wide range of properties may be experimentally accessible.

High-throughput computational screening of metal–organic frameworks with topological diversity for hexane isomer separations

2019 ◽  
Vol 21 (16) ◽  
pp. 8508-8516 ◽  
Author(s):  
Liang Peng ◽  
Qiao Zhu ◽  
Pinglian Wu ◽  
Xuanjun Wu ◽  
Weiquan Cai
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Metal Organic Frameworks ◽  
Equimolar Mixture ◽  
Computational Screening ◽  
Simultaneous Separation ◽  
Metal Organic ◽  
Isomer Separations

A large-scale computational screening of 13 512 MOFs with topological diversity was carried out to search the optimal candidates for the simultaneous separation of two dimethyl butanes from the quinary equimolar mixture of hexane isomers.

Large-scale evaluation of cascaded adsorption heat pumps based on metal/covalent–organic frameworks

2019 ◽  
Vol 7 (43) ◽  
pp. 25010-25019 ◽  
Author(s):  
Wei Li ◽  
Xiaoxiao Xia ◽  
Song Li
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Metal Organic Frameworks ◽  
Heat Pumps ◽  
Adsorption Heat ◽  
Covalent Organic Frameworks ◽  
Scale Evaluation ◽  
Computational Screening ◽  
Metal Organic

High-throughput computational screening of millions of cascaded adsorption heat pumps based on metal–organic frameworks and covalent–organic frameworks.

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