Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset

While perovskite solar cells have reached competitive efficiency values during the last decade, stability issues remain a critical challenge to be addressed for pushing this technology towards commercialisation. In this study, we analyse a large homogeneous dataset of Maximum Power Point Tracking (MPPT) operational ageing data that we collected with a custom-built High-throughput Ageing System in the past 3 years. In total, 2,245 MPPT ageing curves are analysed which were obtained under controlled conditions (continuous illumination, controlled temperature and atmosphere) from devices comprising various lead-halide perovskite absorbers, charge selective layers, contact layers, and architectures. In a high-level statistical analysis, we find a correlation between the maximum reached power conversion efficiency (PCE) and the relative PCE loss observed after 150-hours of ageing, with more efficient cells statistically also showing higher stability. Additionally, using the unsupervised machine learning method self-organising map, we cluster this dataset based on the degradation curve shapes. We find a correlation between the frequency of particular shapes of degradation curves and the maximum reached PCE

The challenge of studying perovskite solar cells’ stability with machine learning

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technology–which has resulted in the accumulation of a significant amount of data. The best example is the “Perovskite Database Project,” which also includes stability-related metrics. From this database, we use data on 1,800 perovskite solar cells where device stability is reported and use Random Forest to identify and study the most important factors for cell stability. By applying the concept of learning curves, we find that the potential for improving the models’ performance by adding more data of the same quality is limited. However, a significant improvement can be made by increasing data quality by reporting more complete information on the performed experiments. Furthermore, we study an in-house database with data on more than 1,000 solar cells, where the entire aging curve for each cell is available as opposed to stability metrics based on a single number. We show that the interpretation of aging experiments can strongly depend on the chosen stability metric, unnaturally favoring some cells over others. Therefore, choosing universal stability metrics is a critical question for future databases targeting this promising technology

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce-data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal halides spanning 3 dimensionalities and 7 space-groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross validated accuracies for dimensionality and space-group classification of 93% and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16{\deg}, which enables an XRD pattern to be obtained and classified in 5.5 minutes or less.Comment: Accepted with minor revisions in npj Computational Materials, Presented in NIPS 2018 Workshop: Machine Learning for Molecules and Material

Photovoltaic potential of tin perovskites revealed through layer-by-layer investigation of optoelectronic and charge transport properties

Tin perovskites are the most promising environmentally friendly alternative to lead perovskites. Among tin perovskites, FASnI3 (CH4N2SnI3) shows optimum band gap, and easy processability. However, the performance of FASnI3 based solar cells is incomparable to lead perovskites for several reasons, including energy band mismatch between the perovskite absorber film and the charge transporting layers (CTLs) for both types of carriers, i.e., for electrons (ETLs) and holes (HTLs). However, the band diagrams in the literature are inconsistent, and the charge extraction dynamics are poorly understood. In this paper, we study the energy band positions of FASnI3 based perovskites using Kelvin probe (KP) and photoelectron yield spectroscopy (PYS) to provide a precise band diagram of the most used device stack. In addition, we analyze the defects within the current energetic landscape of tin perovskites. We uncover the role of bathocuproine (BCP) in enhancing the electron extraction at the fullerene C60/BCP interface. Furthermore, we used transient surface photovoltage (tr-SPV) for the first time for tin perovskites to understand the charge extraction dynamics of the most reported HTLs such as NiOx and PEDOT, and ETLs such as C60, ICBA, and PCBM. Finally, we used Hall effect, KP, and time-resolved photoluminescence (TRPL) to estimate an accurate value of the p-doping concentration in FASnI3 and showed a consistent result of 1.5 * 1017 cm-3. Our findings prove that the energetic system of tin halide perovskites is deformed and should be redesigned independently from lead perovskites to unlock the full potential of tin perovskites.Comment: 22 pages, 5 figure

Improving the Environmental Stability of Methylammonium-Based Perovskite Solar Cells

Perovskite solar cells (PSCs), as an emerging type of photovoltaics, have reached beyond 20% efficiency within a decade. Technoeconomic analysis suggests that PSCs are promising alternatives to the market-dominant silicon, because PSC manufacturing processes are more cost effective due to their solution processing methods. However, the prototypical perovskite material, methylammonium lead iodide (MAPbI3), is environmentally unstable and degrades in the presence of oxygen, light, and moisture. Thus, despite its high initial performance, the degrading performance over time means that the levelized cost of electricity (LCOE) of perovskites is prohibitively high. An improved stability (targeting <0.25% degradation per year or less) could help improve the LCOE of perovskites to surpass silicon. Researchers have been incorporating low-dimensional (LD), such as 0D, 1D, or 2D perovskites, to improve PSCs stability. We can obtain LD perovskites by changing any , , or ions in the 3 structures of high-performing 3D perovskites. The -site cations can be organic or inorganic, which give us a vast number of possible perovskite compounds. Some common examples of 3D perovskite -site cation are methylammonium (MA) and formamidinium (FA). When the -site is larger than FA, it forms LD perovskite structures. This thesis focuses on investigating how to incorporate the LD perovskites as a capping layer to improve the stability of MA-based perovskites, including how to screen and select the -site cations of LD perovskite capping layers that can improve the MAPbI3 absorber stability, how to improve the stability of MAPb(IxBr1–x )3 mixed halide, a wide-bandgap absorber for tandem cells and indoor PV applications, and how to incorporate capping layers in inverted p–i–n PSCs device architectures. These 3 questions are answered by combining high-throughput experiments with machine learning analysis. The optoelectronic, structural, and chemical composition properties of the LD capping-3D perovskite absorber materials are probed to identify the degradation mechanisms using advanced characterization methods. This deeper understanding of perovskite degradation and the strategies to solve the instability issue are critical to push PSCs closer toward commercialization. Keywords: perovskite solar cell, low-dimensional perovskite, capping layer, 2D-3D heterostructures, high-throughput experiment, machine learningPh.D

Interplay of optoelectronic properties and solar cell performance in multidimensional perovskites

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 68-72).Perovskite is an emerging material for photovoltaic application that has reached 22.7% efficiency to date. Despite its excellent properties such as defect tolerance and long carrier lifetime, the high-performing perovskite material, methylammonium lead iodide (MAPI), which has 3D structure, is still unstable. Recent studies have hinted at the possibility of shifting focus from 3D to lower dimensional perovskite structures because lower dimensional structures are more environmentally stable for a longer period than the 3D analogues. We propose a detailed study where PbI₂ is used as the backbone and A-site cations are alloyed with various combinations: methylammonium, dimethylammonium, iso-propylammonium, and t-butylammonium. We measure the perovskite solar cell devices' performance and characterize the solar absorber to understand the optoelectronic properties. It is shown that the addition of large A-site cations change the structures into lower dimension, which increases the bandgap and decreases device performance properties such as efficiency, open-circuit voltage, and short-circuit current. Hence, there is a trade-off between having more stable perovskite and high-performance cell in using large A-site organic cations.by Noor Titan Putri Hartono.S.M

Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset

Abstract While perovskite solar cells have reached competitive efficiency values during the last decade, stability issues remain a critical challenge to be addressed for pushing this technology towards commercialisation. In this study, we analyse a large homogeneous dataset of Maximum Power Point Tracking (MPPT) operational ageing data that we collected with a custom-built High-throughput Ageing System in the past 3 years. In total, 2,245 MPPT ageing curves are analysed which were obtained under controlled conditions (continuous illumination, controlled temperature and atmosphere) from devices comprising various lead-halide perovskite absorbers, charge selective layers, contact layers, and architectures. In a high-level statistical analysis, we find a correlation between the maximum reached power conversion efficiency (PCE) and the relative PCE loss observed after 150-hours of ageing, with more efficient cells statistically also showing higher stability. Additionally, using the unsupervised machine learning method self-organising map, we cluster this dataset based on the degradation curve shapes. We find a correlation between the frequency of particular shapes of degradation curves and the maximum reached PCE

Enhanced visible light absorption for lead-free double perovskite Cs2AgSbBr6.

In a search for Pb-free photovoltaic materials, a double perovskite Cs2AgSbBr6 with an indirect optical bandgap of 1.64 eV has been synthesized. Single crystal X-ray diffraction determined the space group as Fm3[combining macron]m with a = 11.1583(7) Å. The black, as-synthesised compound turned brown after heat treatment at 480 K while the symmetry and crystallinity were preserved. X-ray photoelectron spectroscopy indicated the existence of Sb5+ in the black crystals, suggesting that the dark colour arises from the Sb3+-Sb5+ charge transfer. Furthermore, UV visible spectroscopy and density functional theory calculations have been applied to probe the optical properties and electronic structure

Tailoring capping-layer composition for improved stability of mixed-halide perovskites

Incorporating a low dimensional (LD) perovskite capping layer on top of a perovskite absorber, improves the stability of perovskite solar cells (PSCs). However, in the case of mixed-halide perovskites, which can undergo halide segregation into single-halide perovskites, a systematic study of the capping layer's effect on mixed-halide perovskite absorber is still lacking. This study bridges this gap by investigating how the 1D perovskite capping layers on top of MAPb(IxBr1−x)3 (x = 0, 0.25, 0.5, 0.75, 1) absorbers affect the films' stability. We utilize a new method, dissimilarity matrix, to investigate the image-based stability performance of capping-absorber pair compositions across time. This method overcomes the challenge of analyzing various film colors due to bandgap difference in mixed-halide perovskites. We also discover that the intrinsic absorber stability plays an important role in the overall stability outcome, despite the capping layer's support. Within the 55 unique capping-absorber pairs, we observe a notable 1D perovskite material, 1-methoxynaphthalene-2-ethylammonium chloride (2MeO–NEA–Cl or 9-Cl), that improves the stability of MAPbI3 and MAPb(I0.5Br0.5)3 by at least 8 and 1.5 times, respectively, compared to bare films under elevated humidity and temperature. Surface photovoltage results also show that the accumulation of electrostatic charges on the film surface depends on the capping layer type, which could contribute to the acceleration/deceleration of degradation

How machine learning can help select capping layers to suppress perovskite degradation

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