圧縮誘起格子傾斜による金属ハロゲン化物ペロブスカイト粒界におけるイオン移動の抑制：機械学習分子動力学研究

Ion migration at grain boundaries (GBs) is a key issue leading to the performance degradation of metal halide perovskites (MHPs). Given the weak lattice interactions, the properties of MHPs are highly sensitive to external strain, which is inevitable in practical applications. Nevertheless, a fundamental understanding of the GB behavior under strain is still lacking. Using machine learning molecular dynamics, we demonstrate that uniaxial strain dictates both structural variation and ion migration at a CsPbBr₃ GB. Tensile and strain-free conditions lead to grain sliding along the boundaries, creating versatile migration channels for all species. In contrast, compressive strain triggers lattice tilting within each grain and generates amorphous GB structures, blocking migration channels and ultimately quenching ion mobility. Our work elucidates the pivotal role of strain in determining the performance of MHPs and establishes compressive strain engineering as a promising strategy for enhancing their stability.