High-throughput computational screening approaches have historically been devoted to the discovery of MOFs tailored for gas storage and separations. With the new dataset and machine learning models presented in this work -coupled with an increased understanding of the behavior of common density functional approximations for predicting electronic properties -we anticipate that a computational materials design perspective can be brought to countless new application areas for MOFs. Now hosted on the widely used Materials Project platform (), theorists and experimentalists alike can leverage the data from tens of thousands of quantum-mechanical calculations to accelerate the discovery of promising new MOFs for electronic and optoelectronic applications.