Quantifying transfer learning synergies in infinite-layer and perovskite nitrides, oxides, and fluorides

We combine density functional theory simulations and active learning (AL) of element-embedding neural networks (NNs) to explore the sample efficiency for the prediction of vacancy layer formation energies and lattice parameters inABXninfinite-layer (n= 2) versus perovskite (n= 3) nitrides, oxides, and fluorides in the spirit of transfer learning. Following a comprehensive data analysis from different thermodynamic, structural, and statistical perspectives, we show that NNs model these observables with high precision, using merely∼30%of the data for training and exclusively theA-,B-, andX-site element names as minimal input devoid of any physicala prioriinformation. Element embedding autonomously arranges the chemical elements with a characteristic recurrent topology, such that their relations are consistent with human knowledge. We compare two different embedding strategies and show that these techniques render additional input such as atomic properties negligible. Simultaneously, we demonstrate that AL is largely independent of the initial training set, and exemplify its superiority over randomly composed training sets. Despite their highly distinct chemistry, the present approach successfully identifies fundamental quantum-mechanical universalities between nitrides, oxides, and fluorides that enhance the combined prediction accuracy by up to 16% with respect to three specialized NNs at equivalent numerical effort. This quantification of synergistic effects provides an impression of the transfer learning improvements one may expect for similarly complex materials. Finally, by embedding the tensor product of theBandXsites and subsequent quantitative cluster analysis, we establish from an unbiased artificial-intelligence perspective that oxides and nitrides exhibit significant parallels, whereas fluorides constitute a rather distinct materials class.