Analysis of Metal Organic Frameworks
Typical MOF databases are high-dimensional and sparse that pose a challenge in extracting the key features and trends that could guide the process of MOF discovery. 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.
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. 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. Read more here.