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Combining density functional theory simulations and active learning of neuralnetworks, we explore formation energies of oxygen vacancy layers, latticeparameters, and their correlations in infinite-layer versus perovskite oxidesacross the periodic table, and place the superconducting nickelate and cupratefamilies in a comprehensive statistical context. We show that neural networkspredict these observables with high precision, using only 30-50% of the datafor training. Element embedding autonomously identifies concepts of chemicalsimilarity between the individual elements in line with human knowledge. Basedon the fundamental concepts of entropy and information, active learningcomposes the training set by an optimal strategy without a priori knowledge andprovides systematic control over the prediction accuracy. This offers keyingredients to considerably accelerate scans of large parameter spaces andexemplifies how artificial intelligence may assist on the quantum scale infinding novel materials with optimized properties.

Active learning and element embedding approach in neural networks for infinite-layer versus perovskite oxides