How machine learning can help select capping layers to suppress perovskite degradation
Name
s41467-020-17945-4.pdf
Description
Published version
Size
1.31 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Hartono, Noor Titan Putri
Thapa, Janak
Tiihonen, Armi
Oviedo, Felipe
Batali, Clio
Yoo, Jason J.(Jason Jungwan)
Liu, Zhe
Li, Ruipeng
Marrón, David Fuertes
Bawendi, Moungi G
Date Issued
August 2020
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Hartono, Noor Titan Putri et al. "How machine learning can help select capping layers to suppress perovskite degradation." Nature Communications 11, 1 (August 2020): 4172.
Version
Final published version
Abstract
Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI₃) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules’ low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI₃ film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI₃ stability lifetime by 4 ± 2 times over bare MAPbI₃ and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-020-17945-4
