The idea in this work is to leverage the observed (NPM, property) values to predict the missing ones-i.e., to impute the missing values of, or complete, the NPM-property matrix. A machine learning strategy to complete the NPMproperty matrix is much less expensive and timeconsuming than experimentally measuring or computationally simulating these missing properties. The machine-completed NPM-property matrix is valuable because it can be used to direct higher-fidelity but more expensive (experimental or simulated) measurements towards the most promising materials, thereby using less resources in the search for the optimal NPM for a given application.