Analysis of Metal Organic Frameworks
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. Using MOF as the prototype class of materials to knowledge discovery data mining clean energy.