Role of long-range dipolar interactions in the simulation of the properties of polar crystals using effective atomic potentials

Abstract

Driven by novel approaches and computational techniques, second-principles atomic potentials are nowadays at the forefront of computational materials science, enabling large-scale simulations of material properties with near-first-principles accuracy. However, their application to polar materials can be challenging, particularly when longitudinal-optical phonon modes are active on the material, as accurately modeling such systems requires incorporating the long-range part of the dipole-dipole interactions. In this study, we challenge the influence of these interactions on the properties of polar materials taking BaTiO${}_3$ as paradigmatic example. By comparing models with and without the long-range part of the electrostatic contributions in a systematic way, we demonstrate that even if these interactions are neglected, the models can still provide an overall good description of the material, though they may lead to punctual significant artifacts. Our results propose a pathway to identify when an atomistic potential may be inadequate and needs to be corrected through the inclusion of the long-range part of dipolar interactions.

References in corpus (9)

• Implementation strategies in phonopy and phono3py
• Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference
• A deep potential model with long-range electrostatic interactions
• Self-Consistent Determination of Long-Range Electrostatics in Neural Network Potentials
• Thermodynamics and dielectric response of ${\text{BaTiO}}_3$ by data-driven modeling
• Efficient systematic scheme to construct second-principles lattice-dynamical models
• Universal interatomic potential for perovskite oxides
• Machine-learning interatomic potential for molecular dynamics simulation of ferroelectric KNbO3 perovskite
• Finite-temperature properties of antiferroelectric perovskite $\mathrm{PbZr}{\mathrm{O}}_3$ from deep learning interatomic potential

Cited by in corpus (2)

• Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials
• Finite-temperature properties and the hidden ferroelectric $R3c$ phase of bulk CaTiO${}_3$ from second principles