In this work, we curate a dataset of experimental C2 isotherms in MOFs and train a fully connected artificial neural network (ANN) to screen the CoRE MOF 2019 database for structures that have not previously been explored for ethane-selective C2 separations. This K-C2 dataset consists of over 900 experimentally measured isotherms of acetylene, ethylene, and ethane adsorption in 190 MOFs. We use our models to identify several MOFs that have not previously been investigated for ethane/ethylene separations but are predicted to have high ethane selectivity.