  Crystal-graph attention networks have emerged recently as remarkable tools
for the prediction of thermodynamic stability and materials properties from
unrelaxed crystal structures. Previous networks trained on two million
materials exhibited, however, strong biases originating from underrepresented
chemical elements and structural prototypes in the available data. We tackled
this issue computing additional data to provide better balance across both
chemical and crystal-symmetry space. Crystal-graph networks trained with this
new data show unprecedented generalization accuracy, and allow for reliable,
accelerated exploration of the whole space of inorganic compounds. We applied
this universal network to perform machine-learning assisted high-throughput
materials searches including 2500 binary and ternary structure prototypes and
spanning about 1 billion compounds. After validation using density-functional
theory, we uncover in total 19512 additional materials on the convex hull of
thermodynamic stability and ~150000 compounds with a distance of less than 50
meV/atom from the hull. Combining again machine learning and ab-initio methods,
we finally evaluate the discovered materials for applications as
superconductors, superhard materials, and we look for candidates with large gap
deformation potentials, finding several compounds with extreme values of these
properties.
