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 density functional 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 ${\mathrm{Ba}}_2{\mathrm{AgSi}}_3$, ${\mathrm{Mg}}_2{\mathrm{SiO}}_4$, ${\mathrm{LiAlCl}}_4$, and ${\mathrm{InTe}}_2{\mathrm{O}}_5\mathrm{F}$ 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 DFT-based approach. By proposing a way to developing reliable machine-learning potentials for the crystal structure prediction, this work paves the way to identifying unexplored multinary phases efficiently.

• Received 19 August 2020
• Revised 2 November 2020
• Accepted 2 December 2020

DOI:https://doi.org/10.1103/PhysRevB.102.224104