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 Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting

Nanomaterials ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 159
Author(s):  
Lifeng Li ◽  
Zenan Shi ◽  
Hong Liang ◽  
Jie Liu ◽  
Zhiwei Qiao
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Univariate Analysis ◽  
Metal Organic Frameworks ◽  
Boosted Regression Trees ◽  
Water Harvesting ◽  
Atmospheric Water ◽  
Computational Screening ◽  
Metal Organic ◽  
And Performance

Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H2O from N2 and O2 for 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) and 137,953 hypothetical MOFs (hMOFs). Through the univariate analysis of MOF structure-performance relationships, Qst is shown to be a key descriptor. Moreover, three ML algorithms (random forest, gradient boosted regression trees, and neighbor component analysis (NCA)) are applied to hunt for the complicated interrelation between six descriptors and performance. After the optimizing strategy of grid search and five-fold cross-validation is performed, three ML can effectively build the predictive model for CoRE-MOFs, and the accuracy R2 of NCA can reach 0.97. In addition, based on the relative importance of the descriptors by ML, it can be quantitatively concluded that the Qst is dominant in governing the capture of H2O. Besides, the NCA model trained by 6013 CoRE-MOFs can predict the selectivity of hMOFs with a R2 of 0.86, which is more universal than other models. Finally, 10 CoRE-MOFs and 10 hMOFs with high performance are identified. The computational screening and prediction of ML could provide guidance and inspiration for the development of materials for water harvesting in the atmosphere.

Techno-Economic Analysis of Metal–Organic Frameworks for Adsorption Heat Pumps/Chillers: From Directional Computational Screening, Machine Learning to Experiment

2021 ◽  
Author(s):  
Zenan Shi ◽  
Xueying Yuan ◽  
Yaling Yan ◽  
Yuanlin Tang ◽  
Junjie Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Economic Analysis ◽  
High Performance ◽  
Metal Organic Frameworks ◽  
Heat Pumps ◽  
Adsorption Heat ◽  
Organic P ◽  
Computational Screening ◽  
Metal Organic ◽  
Low Costs

The key to achieving high efficiencies, high performance, and low costs of adsorption heat pumps/chillers (AHPs/ACs) is to choose a suitable adsorbent. A computational screening of 6,013 computation-ready experimental metal–organic...

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.

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