  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.
