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How machine learning can help select capping layers to suppress perovskite degradation -chemRxiv.pdf (3.19 MB)
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How machine learning can help select capping layers to suppress perovskite degradation

preprint
submitted on 29.01.2020 and posted on 30.01.2020 by Noor Titan Putri Hartono, Janak Thapa, Armi Tiihonen, Felipe Oviedo, Clio Batali, Jason Yoo, Zhe Liu, Ruipeng Li, David Fuertes Marrón, Moungi Bawendi, Tonio Buonassisi, Shijing Sun
Environmental stability of perovskite solar cells (PSCs) can be improved by a thin layer of low-dimensional (LD) perovskite sandwiched between the perovskite absorber and the hole transport layer (HTL). This layer, called ‘capping layer,’ has mostly been optimized by trial and error. In this study, we present a machine-learning framework to rationally design and optimize perovskite capping layers. We ‘featurize’ 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI3) thin films, age them under accelerated conditions combining illumination and increased humidity and temperature, and determine features governing stability using random forest regression and SHAP (SHapley Additive exPlanations). We find that a low number of hydrogen-bonding donors and a small topological polar surface area of the organic molecules correlate with increased MAPbI3 film stability. The top performing organic halide salt, 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 morphological and synchrotron-based structural characterization, we found that this capping layer consists of a Ruddlesden-Popper perovskite structure and stabilizes the photoactive layer by “sealing off” the grain boundaries and changing the lead surface chemistry, through the suppression of lead (II) iodide (PbI2) formation and methylammonium loss.

Funding

NSF SusChem grant CBET-1605547

Skoltech grant 1913/R

TOTAL SA research grant Sustng Mbr 9/08, RPP

Alfred Kordelin Foundation and Svenska Tekniska Vetenskaps-akademien i Finland

DOE PVRD Award no. DE-EE0007535

ISN grant W911NF-13-D-0001

NASA grant NNX16AM70H

Eni-MIT Alliance Solar Frontiers Center

MISTI-Spain research grant

Research Mobility Program in the US of UPM

History

Email Address of Submitting Author

nhartono@mit.edu

Institution

Massachusetts Institute of Technology

Country

United States

ORCID For Submitting Author

0000-0002-0748-0620

Declaration of Conflict of Interest

No conflict of interest

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