Proceedings of Online nanoGe Fall Meeting 20 (OnlineNFM20)
Publication date: 4th October 2020
The excellent optoelectronic properties of metal halide perovskites (MHPs) have attracted extensive scientific interests and boosted their application in optoelectronic devices. Despite their attractive optoelectronic properties, their poor stability under ambient conditions remains the major challenge, hindering their large-scale practical applications. In particular, some MHPs undergo spontaneous phase transitions from perovskites to non-perovskites. Compositional engineering via mixing cations or anions has been widely reported to be effective in suppressing such unwanted phase transition. However, the atomistic and electronic origins of the stabilization effect remain unexplored. Here, by combining Density Functional Theory (DFT) calculations and Crystal Orbital Hamilton Population (COHP) analysis, we provide insights for the undesired phase transition of pristine perovskites (FAPbI3, CsPbI3, and CsSnI3) and reveal the mechanisms of the improved phase stability of the mixed compounds (CsxFA1-xPbI3, CsSnyPb1-yI3, and CsSn(BrzI1-z)3). We identify that the phase transition is correlated with the relative strength of the M-X bonds as well as that of the hydrogen bonds (for hybrid compositions) in perovskite and non-perovskite phases. The phase transition can be suppressed by mixing ions, giving rise to either increased bond strength for the perovskite or decreased bond strength in their non-perovskite counterparts. Our results present a comprehensive understanding of the mechanisms for the phase instability of metal halide perovskites and provide design rules for engineering phase-stable perovskite compositions.
S. Tao and J. Jiang acknowledge funding by the Computational Sciences for Energy Research (CSER) tenure track program of Shell and NWO (Project number15CST04-2). Q. Shen and F. Liu acknowledge funding by the Japan Science and Technology Agency (JST) Mirai program (JPMJMI17EA), the MEXT KAKENHI Grant (Grant 26286013, 17H02736), and JSPS International Research Fellow (Faculty of Informatics and Engineering, UEC). Dr. Peter Klaver is acknowledged for his technical support in the computational study in this work.
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