Electrostatic interactions in atomistic and machine-learned potentials for polar material: data

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 novel model 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).

This dataset reports the raw dynamical matrices computed for BaTiO3 using DFPT, the bare short-range GAP potential, and the long-range corrected GAP potential.  A more accurate description of the methodology and all the parameters used to reproduce these data are discussed and reported in the referenced paper.