Export citation

Export citation

Choose format for download:

Download Citation
  • Editors' Suggestion

Electrostatic interactions in atomistic and machine-learned potentials for polar materials

Lorenzo Monacelli and Nicola Marzari

Phys. Rev. B 113, 094101 – Published 2 March, 2026

DOI: https://doi.org/10.1103/7ygl-8db2

Abstract

Long-range electrostatic interactions critically affect polar materials. However, state-of-the-art atomistic potentials, such as neural networks or Gaussian approximation potentials employed in large-scale simulations, often neglect the role of these long-range electrostatic interactions. This study introduces a framework derived from first principles to evaluate the contribution of long-range electrostatic interactions to total energies, forces, and stresses. The model is designed to integrate seamlessly with existing short-range force fields without further first-principles calculations or retraining. The approach relies solely on physical observables, like the dielectric tensor and Born effective charges, that can be consistently calculated from first principles. We demonstrate that the model reproduces critical features, such as the LO-TO splitting and the long-wavelength phonon dispersions of polar materials, with benchmark results on the cubic phase of barium titanate (BaTiO3).

Physics Subject Headings (PhySH)

Authorization Required

We need you to provide your credentials before accessing this content.

References (Subscription Required)

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation