  Surrogate machine-learning models are transforming computational materials
science by predicting properties of materials with the accuracy of ab initio
methods at a fraction of the computational cost. We demonstrate surrogate
models that simultaneously interpolate energies of different materials on a
dataset of 10 binary alloys (AgCu, AlFe, AlMg, AlNi, AlTi, CoNi, CuFe, CuNi,
FeV, NbNi) with 10 different species and all possible fcc, bcc and hcp
structures up to 8 atoms in the unit cell, 15\,950 structures in total. We find
that the deviation of prediction errors when increasing the number of
simultaneously modeled alloys is less than 1\,meV/atom. Several
state-of-the-art materials representations and learning algorithms were found
to qualitatively agree on the prediction errors of formation enthalpy with
relative errors of $<$2.5\% for all systems.
