In the past few years, the remarkable energy conversion efficiency of lead-halide-based perovskite solar cells (PSCs) has drawn extraordinary attention. However, some exposed problems in PSCs such as the low chemical stability and so forth are tough to eliminate. A fundamental understanding of ionic transport at the nanoscale is essential for developing high-performance PSCs based on the anomalous hysteresis current–voltage (I–V) curves and the poor stability. Our work is to understand the ionic transport mechanism by introducing suitable halogen substitution with insignificant impact on light absorption to hinder ion diffusion and thereby to seek a method to improve the stability. Herein, we used first-principles density functional theory (DFT) to calculate the band gaps and the optical absorption coefficients, and the interstitial and the vacancy defect diffusion barriers of halide in the orthogonal phase MAPbX3 (MA = CH3NH3, X = I, Br, I0.5Br0.5) perovskite, respectively. The research results show that a half bromine substitution not only prevents ion migration in perovskite, but also maintains a favorable light absorption capacity. It may be helpful to maintain the PSC’s property of light absorption with a similar atomic substitution. Furthermore, smaller atomic substitution for the halogen atoms may be essential for increasing the diffusion barrier.

As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized interest transitions among items, users’ similarity in sequential dimension should be introduced to further distinguish users’ preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users’ interest sequences (IS) that rank users’ ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users’ longest common sub-IS (LCSIS) and the count of users’ total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users’ IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users’ preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

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