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Visualization

Data accessed from documents or even models and results obtained from machine prediction are of little use to a researcher if they are not presented in a clear and concise manner. We will work with the materials community to design a suitable interface where the data and results from tools can be prepared and presented in a suitable fashion to enable easy sharing, publications and collaborations.

Analysis of Metal Organic Frameworks

View our Metal organic frameworks (MOF) database visualized using manifold learning of Hirshfield Surfaces here.

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?

Read more: Analysis of Metal Organic Frameworks