Elucidating the High-Pressure Phases of MAPbBr3 Using a Machine Learning Force Field

TL;DR

This study uses a novel machine learning force field to accurately simulate high-pressure phase transitions in MAPbBr3, revealing detailed atomic behaviors and phase characteristics that align with experimental observations.

Contribution

The paper introduces a machine learning force field that effectively models phase transitions and local distortions in MAPbBr3 under pressure, providing new insights into its structural dynamics.

Findings

Successful reproduction of pressure-induced phase sequence

Revelation of octahedral tilting and local distortions

Identification of polar and anti-polar domain formations

Abstract

High-pressure phases of the hybrid perovskite MAPbBr3 have been investigated in detail using a novel machine learning force field (MLFF). MLFF simulations successfully reproduce the sequence of pressure-induced phase transitions from the $\alpha$ ($Pm\bar{3}m$) to the $\beta$ ($Im\bar{3}$) and finally the $\gamma$ ($Pnma$/$Pmn2_1$) phase. In the $\alpha$ phase, the simulations confirm the triple-well character of the potential energy surface for octahedral tilting shedding light into the local dynamic distortions. In the $\beta$ phase, our simulations reveal MA sublattice doubling yielding both orientationally disordered and ordered MA ions mirroring experimental observation. This mixed-order phase results from locally frustrated host-guest couplings arising from the in-phase octahedral tilt system ($a^+a^+a^+$). In the high-pressure $\gamma$ phase, we confirm the formation of polar and anti-polar domains, with the latter have higher lifetimes and persist for over 50 ps at pressures above 1.5 GPa. By elucidating the behavior of various phases of MAPbBr3, this work provides a fundamental understanding of how host-guest interactions and octahedral tilting govern the material's properties. Further, the importance of time scales and length scales in characterizing these phases is emphasized.