Data-Mining for Optimal Metal-Organic Frameworks

Cube proposed by: Donald Siegel

Unit: Engineering

About this project: 
Metal-organic frameworks (MOFs) have been identified as promising materials for applications ranging from CO2 capture to the storage of chemical fuels. Despite this promise, additional gains in performance must be realized before MOFs can achieve widespread application. In this regard the vast phase space of possible MOFs emerges as both a blessing and a curse: while there is ample room for the discovery of new MOFs, the systematic testing of existing materials and synthesis of new compounds presents a significant bottleneck. Consequently, a means to quickly screen for optimal MOFs via computation would be of immense value. Data mining will be used to identify correlations between MOF properties and performance, and thereby pinpoint optimized chemistries. Computational and experimental efforts will operate via a feedback mechanism: computation will guide experiments towards promising compounds, and experimental input will be used to refine computational models and identify performance descriptors.