In the realm of energy materials, a team of researchers from the Indian Institute of Technology Madras has made significant strides in understanding the behavior of a promising material, MAPbBr3, under high-pressure conditions. Rashid Rafeek V Valappil, Sayan Maity, and Varadharajan Srinivasan have employed a novel machine learning technique to shed light on the phase transitions and structural dynamics of this hybrid perovskite material.
The researchers utilized a machine learning force field (MLFF) to simulate the behavior of MAPbBr3 under varying pressures. This approach allowed them to accurately reproduce the sequence of phase transitions that occur as the material is subjected to increasing pressure. The material transitions from the alpha phase to the beta phase and finally to the gamma phase. Each of these phases exhibits unique structural characteristics and dynamic behaviors.
In the alpha phase, the simulations confirmed the presence of a triple-well potential energy surface for octahedral tilting. This finding provides insight into the local dynamic distortions that occur within the material. As the material transitions to the beta phase, the simulations revealed a fascinating phenomenon: the doubling of the MA sublattice. This results in a mixed-order phase where both orientationally disordered and ordered MA ions coexist. This mixed-order phase arises from locally frustrated host-guest couplings, driven by the in-phase octahedral tilt system.
In the high-pressure gamma phase, the researchers confirmed the formation of polar and anti-polar domains. Notably, the anti-polar domains exhibited higher lifetimes and persisted for over 50 picoseconds at pressures above 1.5 gigapascals. This detailed understanding of the material’s behavior under high pressure is crucial for its potential applications in the energy sector, particularly in areas such as solar cells and other optoelectronic devices.
The researchers emphasize the importance of considering both time scales and length scales when characterizing these phases. This comprehensive approach provides a fundamental understanding of how host-guest interactions and octahedral tilting govern the material’s properties. By elucidating the behavior of various phases of MAPbBr3, this work paves the way for the development of more efficient and stable energy materials.
This research was published in the journal Physical Review Materials, contributing valuable insights to the field of energy materials science. The findings not only advance our understanding of MAPbBr3 but also highlight the potential of machine learning techniques in studying complex materials. As the energy sector continues to evolve, such advancements are crucial for developing innovative solutions to global energy challenges.
This article is based on research available at arXiv.