Machine-learning interatomic potential for molecular dynamics simulation of ferroelectric KNbO3 perovskite

TL;DR

This paper develops a machine-learning interatomic potential for KNbO3, enabling accurate and efficient molecular dynamics simulations of ferroelectric properties, phase transitions, and domain wall behavior, advancing the computational study of lead-free ferroelectric materials.

Contribution

The study introduces a deep neural network-based interatomic potential trained on first-principles data for KNbO3, improving simulation accuracy and efficiency for ferroelectric perovskites.

Findings

Accurately predicts atomic forces, energies, and elastic properties.

Replicates phonon dispersion and phase transition behavior.

Effectively models domain wall dynamics.

Abstract

Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently demonstrated to be an excellent candidate for sustainable development. Molecular dynamics is a versatile theoretical calculation approach for the investigation of the dynamical properties of ferroelectric perovskites. However, molecular dynamics simulation of ferroelectric perovskites has been limited to simple systems, since the conventional construction of interatomic potential is rather difficult and inefficient. In the present study, we construct a machine-learning interatomic potential of KNbO3 (as a representative system of (K,Na)NbO3) by using a deep neural network model. Including first-principles calculation data into the training dataset ensures the quantum-mechanics accuracy of the interatomic potential. The molecular dynamics based on machine-learning interatomic potential shows good agreement with the first-principles calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic properties, and phonon dispersion. In addition, the interatomic potential exhibits satisfactory performance in the simulation of domain wall and temperature-dependent phase transition. The construction of interatomic potential based on machine learning could potentially be transferred to other ferroelectric perovskites and consequently benefits the theoretical study of ferroelectrics.