Learning targeted materials properties from data

We compare several strategies using a data set of 223 M$_2$AX family of compounds for which the elastic properties [bulk ($\mathrm{B}$), shear ($\mathrm{G}$), and Young's ($\mathrm{E}$) modulus] have been computed using density functional theory. The strategy is decomposed into two steps: a \emph{regressor} is trained to predict elastic properties in terms of elementary orbital radii of the individual components of the materials; and a \emph{selector} uses these predictions to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties. We examine how the choice of data set size, regressor and selector impact the results.