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Published on: February 27, 2017
Machine-Learning-Assisted Investigation on Benign Ion Migration in Metal Halide Perovskites
Ning-Jing Hao1, Rui Dai1, Chuan-Jia Tong1
1Institute of Quantum Physics, Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super-Microstructure and Ultrafast Process, School of Physics, Central South University, Changsha 410083, China.
Abstract:
Defect-assisted ion migration is one of the important issues that results in instability and non-radiative losses in hybrid organic-inorganic metal halide perovskite solar cells. In this work, based on the deep potential (DP) model, a long-time-scale molecular dynamics (MD) simulation has been employed to capture the interstitial-assisted iodine migration process. The results indicate that, when interstitial iodine (Ii) begins to migrate, the serious structural distortion becomes mild, weakening the electron-vibration interaction. The deep trap state induced by the iodine trimer undergoes a "deep-shallow-deep" dynamic process, which ultimately leads to an improvement of the carrier lifetime during the interstitial-assisted iodine migration process. Our work confirms that different dynamic processes are strongly correlated in halide perovskites and demonstrates that ion migration, considered to be detrimental, can become benign in a particular case. The reported results provide new fundamental insight to improve the efficiency of CH3NH3PbI3 perovskite solar cells.
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