Long-range interaction effects on the phase transition, mechanical effect, and electric field response of BaTiO3 by machine learning potentials

Bulk materials are governed by both short-range and long-range interactions, both of which are naturally captured in conventional density functional theory calculations through Ewald summation of electrostatic contributions. In contrast, machine learning potentials (MLPs) typically rely on local atomic environment descriptors, and long-range interactions are often neglected. Such approximations may introduce systematic energetic errors and lead to inaccuracies in predicted material properties. To systematically investigate the impact of long-range interactions in ferroelectric BaTiO3 within the framework of MLPs, we developed a long-range MACELES model and compared its performance with that of the previously reported BaTiO3 MACE model across four key properties: phonon dispersion, phase transition behavior, mechanical response, and ferroelectric properties, including dielectric constants. We find that qualitative behaviors, including phase transitions, stress-induced polarization switching, and polarization–electric field hysteresis, are consistently reproduced by both models. In contrast, quantitative properties such as transition temperatures, elastic constants, and dielectric constants exhibit systematic improvements in the MACELES model, highlighting the importance of incorporating long-range electrostatic interactions for accurately describing the structural and dielectric responses of BaTiO3. These results suggest that while long-range interactions improve quantitative accuracy, their omission does not significantly alter the qualitative ferroelectric behavior of BaTiO3.