How machine learning can help select capping layers to suppress perovskite degradation
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.
- Authors:
-
[1];
[1];
[1];
[1];
[1]
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Univ. Politécnica de Madrid (Spain)
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); Alfred Kordelin Foundation; Institute for Soldier Nanotechnology (ISN); National Aeronautics and Space Administration (NASA)
- OSTI Identifier:
- 1670673
- Report Number(s):
- BNL-219930-2020-JAAM
Journal ID: ISSN 2041-1723
- Grant/Contract Number:
- SC0012704; 1541959; DMR-1419807; CBET-1605547; 1913/R; EE0007535; W911NF-13-D-0001; NNX16AM70H
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Hartono, Noor Putri, Thapa, Janak, Tiihonen, Armi, Oviedo, Felipe, Batali, Clio, Yoo, Jason J., Liu, Zhe, Li, Ruipeng, Marrón, David Fuertes, Bawendi, Moungi G., Buonassisi, Tonio, and Sun, Shijing. How machine learning can help select capping layers to suppress perovskite degradation. United States: N. p., 2020.
Web. doi:10.1038/s41467-020-17945-4.
Hartono, Noor Putri, Thapa, Janak, Tiihonen, Armi, Oviedo, Felipe, Batali, Clio, Yoo, Jason J., Liu, Zhe, Li, Ruipeng, Marrón, David Fuertes, Bawendi, Moungi G., Buonassisi, Tonio, & Sun, Shijing. How machine learning can help select capping layers to suppress perovskite degradation. United States. https://doi.org/10.1038/s41467-020-17945-4
Hartono, Noor Putri, Thapa, Janak, Tiihonen, Armi, Oviedo, Felipe, Batali, Clio, Yoo, Jason J., Liu, Zhe, Li, Ruipeng, Marrón, David Fuertes, Bawendi, Moungi G., Buonassisi, Tonio, and Sun, Shijing. Thu .
"How machine learning can help select capping layers to suppress perovskite degradation". United States. https://doi.org/10.1038/s41467-020-17945-4. https://www.osti.gov/servlets/purl/1670673.
@article{osti_1670673,
title = {How machine learning can help select capping layers to suppress perovskite degradation},
author = {Hartono, Noor Putri and Thapa, Janak and Tiihonen, Armi and Oviedo, Felipe and Batali, Clio and Yoo, Jason J. and Liu, Zhe and Li, Ruipeng and Marrón, David Fuertes and Bawendi, Moungi G. and Buonassisi, Tonio and Sun, Shijing},
abstractNote = {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.},
doi = {10.1038/s41467-020-17945-4},
journal = {Nature Communications},
number = 1,
volume = 11,
place = {United States},
year = {Thu Aug 20 00:00:00 EDT 2020},
month = {Thu Aug 20 00:00:00 EDT 2020}
}
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Fig. 1: The workflow of the study and aging test result over time. a Schematic overview of this study, aimed at developing design rules for capping layer of perovskite solar cells. b the raw image changes for tetrapropylammonium iodide (TPAl)-capped, phenyltriethylammonium iodide (PTEAl)-capped, which have similar molecular weights, and baremore »
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