  Combining density functional theory simulations and active learning of neural
networks, we explore formation energies of oxygen vacancy layers, lattice
parameters, and their correlations in infinite-layer versus perovskite oxides
across the periodic table, and place the superconducting nickelate and cuprate
families in a comprehensive statistical context. We show that neural networks
predict these observables with high precision, using only 30-50% of the data
for training. Element embedding autonomously identifies concepts of chemical
similarity between the individual elements in line with human knowledge. Based
on the fundamental concepts of entropy and information, active learning
composes the training set by an optimal strategy without a priori knowledge and
provides systematic control over the prediction accuracy. This offers key
ingredients to considerably accelerate scans of large parameter spaces and
exemplifies how artificial intelligence may assist on the quantum scale in
finding novel materials with optimized properties.
