  Recent application of neural networks (NNs) to modeling interatomic
interactions has shown the learning machines' encouragingly accurate
performance for select elemental and multicomponent systems. In this study, we
explore the possibility of building a library of NN-based models by introducing
a hierarchical NN training. In such a stratified procedure NNs for
multicomponent systems are obtained by sequential training from the bottom up:
first unaries, then binaries, and so on. Advantages of constructing NN sets
with shared parameters include acceleration of the training process and intact
description of the constituent systems. We use an automated generation of
diverse structure sets for NN training on density functional theory-level
reference energies. In the test case of Cu, Pd, Ag, Cu-Pd, Cu-Ag, Pd-Ag, and
Cu-Pd-Ag systems, NNs trained in the traditional and stratified fashions are
found to have essentially identical accuracy for defect energies, phonon
dispersions, formation energies, etc. The models' robustness is further
illustrated via unconstrained evolutionary structure searches in which the NN
is used for the local optimization of crystal unit cells.
