  Prediction of the stable crystal structure for multinary (ternary or higher)
compounds with unexplored compositions demands fast and accurate evaluation of
free energies in exploring the vast configurational space. The machine-learning
potential such as the neural network potential (NNP) is poised to meet this
requirement but a dearth of information on the crystal structure poses a
challenge in choosing training sets. Herein we propose constructing the
training set from densityfunctional-theory (DFT) based dynamical trajectories
of liquid and quenched amorphous phases, which does not require any preceding
information on material structures except for the chemical composition. To
demonstrate suitability of the trained NNP in the crystal structure prediction,
we compare NNP and DFT energies for Ba2AgSi3, Mg2SiO4, LiAlCl4, and InTe2O5F
over experimental phases as well as low-energy crystal structures that are
generated theoretically. For every material, we find strong correlations
between DFT and NNP energies, ensuring that the NNPs can properly rank energies
among low-energy crystalline structures. We also find that the evolutionary
search using the NNPs can identify low-energy metastable phases more
efficiently than the DFTbased approach. By proposing a way to developing
reliable machine-learning potentials for the crystal structure prediction, this
work will pave the way to identifying unexplored multinary phases efficiently.
