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Predictive Modeling of Ion Migration Induced Degradation in Perovskite Solar Cells.

Vikas NandalPradeep R Nair
Published in: ACS nano (2017)
With excellent efficiencies being reported from multiple laboratories across the world, device stability and the degradation mechanisms have emerged as the key aspects that could determine the future prospects of perovskite solar cells. However, the related experimental efforts remain scattered due to the lack of any unifying theoretical framework. In this context, here we provide a comprehensive analysis of ion migration effects in perovskite solar cells. Specifically, we show that (a) the effect of ionic charges is almost indistinguishable from that of dopant ions, (b) ion migration could lead to simultaneous improvement in Voc and degradation in Jsc-an observation which is beyond the realm of mere parametric variation in carrier mobility and lifetime, (c) champion devices are more resilient toward the ill effects of ion migration; (d) we propose characterization schemes to determine both magnitude and polarity of ionic species, and finally, (e) we illustrate that ion migration could be differentiated from ion redistribution based on the distinct trends in performance degradation. Our results, supported by detailed numerical simulations and direct comparison with experimental data, are of broad interest and provide a much needed predictive capability toward the research on performance degradation mechanisms in perovskite solar cells.
Keyphrases
  • perovskite solar cells
  • machine learning
  • molecular dynamics
  • artificial intelligence
  • big data
  • endothelial cells
  • genetic diversity