Quantitative evaluation of nuclear quantum effects on the phase transitions in BaTiO3 using large-scale molecular dynamics simulations based on machine learning potentials

TL;DR

This study uses machine learning-based molecular dynamics with quantum thermal bath methods to quantitatively assess nuclear quantum effects on phase transitions in BaTiO3, revealing their significance even at room temperature.

Contribution

It introduces a large-scale ML potential-based MD approach incorporating NQEs via QTB, providing accurate phase diagrams consistent with experimental and path-integral results.

Findings

NQEs lower phase transition pressures and temperatures

Large cell sizes and specific friction coefficients are essential for accuracy

NQEs remain significant at room temperature

Abstract

The machine learning potential (MLP) based molecular dynamics (MD) method was applied for constructing the pressure-temperature phase diagram in the barium titanate (BaTiO3) crystals. The nuclear quantum effects (NQEs) on the phase transitions were quantitatively evaluated from the difference in the phase transition pressures between the NQEs-incorporated and classical simulations. In this study, the quantum thermal bath (QTB) method was used for incorporating the NQEs. The constructed phase diagrams verified that the NQEs lower the phase transition temperatures and pressures. The NQEs are more significant at lower temperatures but cannot be ignored even at room temperature. The phase diagram in the QTB-based MLPMD is in good agreement with those of the previous studies based on dielectric measurements and path-integral based simulations. In addition, this study clarified that the large cell size (a 16x16x16 or larger cell) and friction coefficient (>= 15 THz) are required for accurately reproducing the phase transitions during the QTB-MD simulations.