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The identification of descriptors of materials properties and functions thatcapture the underlying physical mechanisms is a critical goal in data-drivenmaterials science. Only such descriptors will enable a trustful and efficientscanning of materials spaces and possibly the discovery of new materials.Recently, the sure-independence screening and sparsifying operator (SISSO) hasbeen introduced and was successfully applied to a number of materials-scienceproblems. SISSO is a compressed-sensing based methodology yielding predictivemodels that are expressed in form of analytical formulas, built from simplephysical properties. These formulas are systematically selected from an immensenumber (billions or more) of candidates. In this work, we describe a powerfulextension of the methodology to a 'multi-task learning' approach, whichidentifies a single descriptor capturing multiple target materials propertiesat the same time. This approach is specifically suited for a heterogeneousmaterials database with scarce or partial data, e.g., in which not allproperties are reported for all materials in the training set. As showcaseexamples, we address the construction of materials-properties maps for therelative stability of octet-binary compounds, considering several crystalphases simultaneously, and the metal/insulator classification of binarymaterials distributed over many crystal-prototypes.

Simultaneous Learning of Several Materials Properties from Incomplete Databases with Multi-Task SISSO