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Metal organic frameworks (MOFs) are one of the most exciting advances in solid state materials science. They are crystalline materials assembled with metal clusters and organic linkers, which have tailorable pore sizes, pore geometries, high void fractions, and large surface areas. Those features enable a wide applications of MOFs in many fields, including gas storage, separation, catalysis, and carbon capture. Since their first discovery, thousands of MOFs have been experimentally synthesized. The rich and still growing database of MOFs have also raised a crucial challenge: how does one identify the most promising structures, among the thousands of possibilities, for a particular application?
As synthesizing and testing a large number of MOF is not feasible in practice, the high-throughput computational screening of the MOF database can help expedite the experimental efforts. However, typical MOF database is high-dimensional and sparse that pose the challenge of extracting the key features and trends that could guide the discovery process. To address this issue, we develop a library of MOF fingerprints based on their geometric and chemical bonding interactions. Such fingerprints are computational ready to be analyzed with various machine learning methods.
For this tool, correlative analysis of metal organic frameworks mappings are displayed.
We demonstrate the use of non-linear manifold learning methods to map the connectivity and extent of similarity between diverse metal-organic framework (MOF) structures in terms of their surface areas by taking into account both crystallographic and electronic structure information. The fusing of geometric and chemical bonding information is accomplished by using 3-dimensional Hirshfeld surfaces of MOF structures, which encode both chemical bonding and molecular geometry information. A comparative analysis of the geometry of Hirshfeld surfaces is mapped into a low dimensional manifold through a graph network where each node corresponds to a different compound. By examining nearest neighbor connections, we discover structural and chemical correlations among MOF structures that would not have been discernible otherwise. Examples of the types of information that can be uncovered using this approach are given.
Please see "Correlative analysis of metal organic framework structures through manifold learning of Hirshfeld surfaces"; Xiaozhou Shen, Tianmu Zhang, Scott Broderick, and Krishna Rajan; Molecular Systems Design and Engineering; DOI: 10.1039/c8me00014j(2018), for more information.
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