Takafumi Ogawa | Searcharxiv

arXiv2023

Lattice dynamics and ferroelectric properties of the nitride perovskite ${\mathrm{LaWN}}_{3}$

Using first-principles calculations we examine the crystal structures and phase transitions of nitride perovskite LaWN$_3$. Lattice dynamics calculations indicate that the ground-state structure belongs to space group $R3c$. Two competitive phase transition pathways are identified which are characterized by symmetry-adapted distortion modes. The results suggest that $R3c$ LaWN$_3$ should be an excellent ferroelectric semiconductor: its large spontaneous polarization of around 61 $μ$C/cm$^2$ is comparable to that of PbTiO$_3$, and its band gap is about 1.72 eV. Ferroelectricity is found to result from the \emph{B}-site instability driven by hybridization between W-5$d$ and N-2$p$ orbitals. These properties make LaWN$_3$ an attractive candidate material for use in ferroelectric memory devices and photovoltaic cells.

cond-mat.mtrl-sci↗

arXiv2022

Facilitating {\it ab initio} configurational sampling of multicomponent solids using an on-lattice neural network model and active learning

We propose a scheme for {\it ab initio} configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventional way: the NNPs are trained to predict the energies of relaxed structures from the perfect lattice with configurational disorder instead of the usual way of training to predict energies as functions of continuous atom coordinates. An active learning scheme is employed to obtain a training set containing configurations of thermodynamic relevance. This enables bypassing of the structural relaxation procedure which is necessary when applying conventional NNP approaches to the lattice configuration problem. The idea is demonstrated on the calculation of the temperature dependence of the degree of A/B site inversion in three spinel oxides, MgAl$_2$O$_4$, ZnAl$_2$O$_4$, and MgGa$_2$O$_4$. The present scheme may serve as an alternative to cluster expansion for `difficult' systems, e.g., complex bulk or interface systems with many components and sublattices that are relevant to many technological applications today.

physics.comp-ph↗