Machine learning analysis on stability of perovskite solar cells

Machine learning analysis on stability of perovskite solar cells

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Solar Energy Materials & Solar Cells xxx (xxxx) xxx

Contents lists available at ScienceDirect

Solar Energy Materials and Solar Cells journal homepage: http://www.elsevier.com/locate/solmat

Machine learning analysis on stability of perovskite solar cells �la Odabas¸ı, Ramazan Yıldırım * Çag Department of Chemical Engineering, Bo�gaziçi University, 34342, Bebek, Istanbul, Turkey

A R T I C L E I N F O

A B S T R A C T

Keywords: Perovskite solar cells Data mining Machine learning Association rule mining Stability Knowledge extraction

In this work, a dataset containing long-term stability data for 404 organolead halide perovskite cells was con­ structed from 181 published papers and analyzed using machine-learning tools of association rule mining and decision trees; the effects of cell manufacturing materials, deposition methods and storage conditions on cell stability were investigated. For regular cells, mixed cation perovskites, multi-spin coating as one-step deposition, DMF þ DMSO as precursor solution and chlorobenzene as anti-solvent were found to have positive effects on stability; SnO2 as ETL compact layer, PCBM as second ETL, inorganic HTLs or HTL-free cells, LiTFSI þ TBP þ FK209 and F4TCNQ as HTL additives and carbon as back contact were also found to improve stability. The cells stored under low humidity were found to be more stable as expected. The degradation was slightly faster in inverted cells under humid condition; the use of some materials (like mixed cation perovskites, PTAA and NiOx as HTL, PCBM þ C60 as ETL, and BCP interlayer) were found to result in more stable cells.

1. Introduction Organolead halide perovskite solar cells (PSCs) have been attracted great attention in recent years, and the power conversion efficiency (PCE) has reached to 23.7% in less than 10 years [1]. However, the challenges in long term stability remained unsolved preventing the commercialization of this promising technology because the efficiency, stability and cost (golden triangle) are the basic requirement for any practical application [2]. Consequently, there has been a considerable shift in research focus to stability; indeed, the number of research arti­ cles involving the stability has been increased in recent years as evident from Fig. 1 (from Web of Science search with the keyword search of perovskite solar and stability in topic segment on April 03, 2019). There has been also considerable progress in the stability of PSCs; for example, Arora et al. [3] and Christians et al. [4] reported 1000 h stability under illumination while the cells retaining 95% of the initial cell efficiency by modifying their HTL. Similarly, Grancini et al. [5] achieved stability of solar modules more than 10000 h without PCE loss using 2D/3D perovskites. There are large numbers of publications covering the long-term stability of the perovskite cells. These publications usually report the effects of individual materials (like individual perovskite) and deposi­ tion methods (like one or two step) as well as the storage conditions (light, humidity, temperature and oxygen) used in those works; there are also reports that compare the effects of material alternatives

investigated in the same work or in various publications. Consequently, there is a massive accumulation of data, which likely contain invaluable information on this subject; however, it is not easy to utilize this collection of data by naked eyes because it is too complex (too many variables with too many options), non-uniform and scattered over a large number of publications. Sufficiently large datasets can be con­ structed from the related publications and can be analyzed using ma­ chine learning tools to identify the general patterns, trends and significant factors; then, the results obtained to understand the experi­ ence in the field can help improving stability further. Indeed, we have implemented this approach in various fields of energy such as water gas shift reaction [6], CO oxidation [7], CO2 adsorption [8], dry reforming of methane [9], biodiesel production [10], and water splitting [11], and obtained significant success in making some valuable generalization. We recently published a work involving the efficiency analysis of perovskite solar cells using machine learning tools [12]; there are also some other recent attempts to understand the various aspect of this potentially important technology using machine learning [13–17]. However, as far as we know, there is no published work to understand the effects of cell manufacturing materials and methods on cell stability by analyzing the results of large number of published experimental works performed for this purpose. In this work, we constructed a dataset containing the stability profiles (power conversion efficiency versus time data) of 404 cells (from 181 publications), which were manufac­ tured and tested under various conditions. Then, we analyzed the

* Corresponding author. E-mail address: [email protected] (R. Yıldırım). https://doi.org/10.1016/j.solmat.2019.110284 Received 1 August 2019; Received in revised form 18 October 2019; Accepted 7 November 2019 0927-0248/© 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Çağla Odabaşı, Ramazan Yıldırım, Solar Energy Materials & Solar Cells, https://doi.org/10.1016/j.solmat.2019.110284

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reported because it may differ significantly (more than 0–30%) in different laboratory conditions and has different effects on cell stability. We used stability versus time plots to investigate the effects of ambient conditions in Section 3.1. For machine learning analyses (Section 3.2 and Section 3.3), we eliminated the data involving the extreme condi­ tions (under artificial illumination and high temperature) to see the effects of materials and methods affecting stability clearly; the data involving extreme conditions were not suitable for machine learning analysis. Hence, at the end, 181 papers containing 404 stability profiles (efficiency versus time plots) were used for the analysis, and this dataset is provided as an embedded file in Supplementary Data file (Section S5). Figs. 3–6, however, were plotted using the initial data that also contains extreme conditions.

Fig. 1. Number of publications on stability of perovskite solar cells.

dataset using machine learning tools of association rule mining and decision trees to determine the effects of cell materials and perovskite deposition methods, which were frequently reported to be effective for cell stability; we also included the effects of testing conditions (humid­ ity, temperature and illumination) in our analysis. Although we recog­ nize the necessity of standardized measuring and reporting protocols as suggested by Saliba et al. [18] for a more reliable analysis, such pub­ lished work on long term stability are quite limited. Consequently, we used the stability tests reported in reliable sources, even if they do not follow such protocols; because they still contain useful information to understand the effects of different materials, manufacturing methods and storage conditions on the degradation patterns of PSCs.

2.2. Computational details The effects of ambient conditions on stability were analyzed using simple descriptive statistics (Figs. 3–6). For this purpose, the averages of normalized PCEs for the cells tested/stored under various ambient conditions were computed for each day using the data collected (before reducing for machine learning analysis) and plotted against time to capture degradation patterns under specific conditions. The plots were continued as long as the number of data points were higher or equal to five, and terminated when the number of data points decreased under five to preserve the reliability of plots. For machine learning analyses, in addition to the cell containing uncertainties in testing conditions, we excluded the data if the stability tests were performed under extreme conditions (such as strong illumi­ nation or high temperatures) that are not comparable to the majority of the cells, which were tested under ambient (or close) conditions. We also excluded the measurements performed with encapsulated cells because the purpose, type and the effects of encapsulating materials were not standard. Additionally, we combine the data taken under zero or low (0–30%RH) humidity conditions in machine learning analysis consid­ ering the similar degradation patterns observed in descriptive analysis (Figs. 3c and 4a). The association rule mining was used to analyze the impact of input variables and to find the most frequently used factors for stable cells. arules package [20] of R Studio software [21] was used in which apriori algorithm was employed. Three parameters were used for the analysis; support, confidence and lift. Support is the fraction of cells with a specific factor in stable cells to all data points. Confidence is the fraction of cell with that factor in all high stable cells, and the lift is the fraction of cell with that factor in stable cells to fraction of cells with that factor in total data points. This value was also used as the indicator of the significance of a factor for high stability (it explained in Results and Discussion in more details). The change of lift values was observed for the cells stable more than 15, 30 and 60 days. Normalized PCE values were used in the analyses. However, considering that the absolute value of initial PCE is also important to see the potential of the cell, we performed the analysis covering both the cells having the initial PCE � 10% and all data points. In decision tree analysis, the dataset was divided into three classes; cells preserved 80% of initial efficiency more than 60 days (Class A),

2. Material and Methods 2.1. Construction of database The dataset was created from research articles published in various journals in ACS, Elsevier, Wiley and RSC databases as well as Nature Group and Science between 2016 and 2018 (until August 2, 2018) using the keywords of perovskite solar and stability in topic field. The related papers were sorted by relevance. The data were collected manually as they were given in the text, tables or the plots in which the data were extracted using Digitizelt software [19]. The normalized PCE values changing upon time were noted in daily basis for each cell and used in the analyses. The period in which the cell preserved more than 80% of its initial PCE was used as the output variable for association rule mining and decision tree models. The type of the major materials in all layers (perovskite, ETL, HTL and back contact) and perovskite deposition techniques (one or two-step deposition procedures and techniques used during the deposition) were used as the input variables (Table S1). At initial stage, we collected and analyzed more articles than we reported in this communication. However, some variables such as cell storage temperature, light and humidity values were not clearly re­ ported in some publications. Sufficient information was generally given for the cells stored in extreme conditions like high temperatures or under constant illumination (probably due to the intentional efforts to create those conditions), but less care was taken for mild conditions probably with the assumption that the result will not change with the small changes in environmental conditions (like a few degrees change in room temperature). As a matter of fact, these assumptions are not so bad as indicated in analysis in Section 3.1 if the change is mild in tempera­ ture, humidity (up to 30%) and light conditions (darkness or room light). Considering that the conditions would be reported if they were extreme or uncommon, we also made some assumptions to be able to utilize as much many papers as possible. For light conditions, we assumed the cells were stored under room light if there is no information for lighting. Similarly, we assumed that the cells were stored at room temperature (around 25 � C) if the temperature was not provided. However, we eliminated the data if the storage humidity values were not

Fig. 2. Factors affecting degradation. 2

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Fig. 3. Comparison of degradation in regular (n-i-p) cells (a) under different lightning conditions (zero humidity, room temperature), (b) at different temperatures (room light, zero humidity), (c) at different humidity ranges (dark, room temperature).

Fig. 4. Comparison of degradation in regular (n-i-p) cells (a) under different humidity levels at room-light, (b) under different humidity levels under illumination, (c) at different temperatures under illumination (zero humidity).

cells preserved 80% of initial efficiency within 7–60 days (Class B) and cells preserved 80% of initial efficiency less than seven days (Class C). We constructed decision trees with/without PCE limitation similar to association rule mining. This division (except Class A) was decided depending on the distribution of data as the number of data points in classes should be balanced; hence, we divided the remaining data from Class A into two equal parts. Then, we performed random sampling of Class A to prevent class imbalance (Class A in dataset of PCE� 10% and all data points was sampled 3.5 and 3.7 times, respectively). Random sampling of classes was implemented using dplyr package [22] of RStudio. In database of inverted cells, we did not have enough data

points for Class A for both PCE�10 (only 8 in 77 data points) and without PCE consideration (only 8 in 91 data points), so we could not construct a decision tree. Minimum split number, maximum depth and complexity parameter were optimized using 5-fold cross validation, then, fine-tuned to have a tree with the highest classification accuracy as well as with reasonable generalizable results in the terminal nodes. Then, the model reconstructed using all data points with optimized parameters and used for the rule deduction and error estimation. The variables with (*) sign in trees come from one article but appeared in tree due to random sampling. These variables should be treated cautiously. 3

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Fig. 5. Comparison of degradation in various cell structures at room temperature with zero humidity (a) regular vs. inverted in darkness, (b) planar vs. mesoporous (regular) in darkness, (c) regular vs. inverted under room light, (d) planar vs. mesoporous (regular) under room light, (e) planar vs. mesoporous (regular) under illumination.

3. Results and Discussion

polyvinylpyrrolidone (PVP) [37], polyvinyl alcohol (PVA) [38] or C70 fullerene [39] were found to affect stability of the perovskite cells. Similarly, anti-solvent treatment has an effect on stability as well due to the similar reasons with precursor solution [40–44]. In the following sections, we will briefly discuss the effects of testing and storage con­ ditions and then move to the machine learning analysis of materials and perovskite deposition methods used in cells manufacturing.

The moisture, oxygen, ambient temperature and light were found to affect the device stability by various investigators (Fig. 2) [23,24]. In addition to the environmental conditions, the stability highly depends on the material type, composition, deposition method and precursor solution used in the perovskite as well as the electron transfer (ETL) [25] and hole transfer (HTL) layers. For example, using triple cation perov­ skite (containing MA, FA and Cs cations together) was found to improve stability significantly by hindering yellow phase impurities and forming uniform perovskite grains [26]. 2D/3D perovskites were also reported to be promising for high stability [27]. Perovskite deposition procedure (more specifically one or two step deposition) influences the quality, homogeneity and surface coverage of the perovskite film, and conse­ quently may play significant role in stability in air by resisting the moisture in the environment [28–30]. Perovskite precursor solution has also effects on stability because its composition and structure directly affects the perovskite morphology and crystallization [31]. For example, employing different lead source [32], solvent type [33] or using addi­ tives in conventional precursor solutions such as water [34], PDMS–urea (a hydrophobic polymer) [35], polyethylene glycol (PEG) [36],

3.1. Exploratory data analysis for effect of storage and testing conditions The testing and storage conditions (moisture, oxygen, ambient temperature and light) strongly affect the device stability. Figs. 3–6 show the change of average normalized PCEs (PCE/initial PCE) due to degradation with time as the indicator of (in)stability (we will use a more meaningful measure for stability for machine learning analysis in the following sections); hence, all plots start with the normalized PCE of one (for day one) and continued until the day in which the number of cells dropped to less than five. Considering that the number of data points are sufficiently large, the comparison of average performances should represent the trend in dataset (and therefore in the literature) reasonably well, and provide some insight for the effects of storage 4

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Fig. 6. Comparison of humidity effect on various cell structures in darkness and at room temperature (a) regular/inverted (0–30% RH), (b) mesoporous/planar regular (0–30% RH), (c) regular/inverted (30–60% RH), (d) mesoporous/planar regular (30–60% RH), (e) regular/inverted (above 60% RH) cells.

conditions. The cells stored in darkness, under room light and under illumination (intentional artificial AM1.5G, 1000 W/m2 irradiation) were compared in Fig. 3.a and Fig. S1 for regular (n-i-p) and inverted (p-i-n) cells, respectively (there were not sufficient data for illumination test in inverted structures, hence there is no plot for that in SI). We limited this comparison for the cells, which were stored at room temperature (up to 30 � C) and zero-humidity environment to see the effect of light clearly; we will also present and discuss the joint effects of light and humidity later. It was reported that both UV-light and oxygen are required to initiate decomposition, and iodide anions are oxidized in the presence of light. In the presence of these two factors, a free electron and iodine are formed, then the free electron interacts with environmental species (such as O2 and CO2) to form free radicals (O2 and CO2 ). These free radicals take acidic proton from organic cation of perovskites to produce volatile methylamine as shown in Equation (1) and Equation (2) [45]. � hv 2CH3 NH3 PbI3 þ 1 2O2 !2CH3 NH2 þ H2 O þ I2

(1)

� hv CH3 NH3 PbI3 þ CO2 !CH3 NH2 þ HCO2 þ 1 2I2

(2)

effects of the TiO2, which is the most common ETL, causes UV-light degradation [46]. It was reported that the degradation on per­ ovskite/TiO2 interface is initiated with the extraction of an electron and PbI2 forms with releasing CH3NH2 and HI (Equation (3)) [45]. CH3NH3PbI3 ⇌ PbI2 þ CH3NH2 þ HI

(3)

As will be discussed below, illumination also causes additional decomposition under humid conditions indicating that they are inter­ acting [47]. For example, Rajan Jose et al. found that perovskite showed a minor degradation in darkness with a humidity level above 70% whereas, upon light exposure, the degradation rate increased signifi­ cantly [48]. Similarly, the illumination and oxygen seem to also interact; the oxygen was reported to cause photo-oxidation in the presence of light [23,49,50]. We also analyzed the effect of storage temperature by using the cells stored under constant humidity (zero percent) and room light; the re­ sults in dark conditions could not be plotted due to the insufficient number of data points. At room light conditions, the negative effect of high temperature was quite clear for both cell structures (Fig. 3b and Fig. S2) as high temperature was reported to cause structural phase transitions in perovskites [51,52]. Misra et al. [53] found that the degradation occurred by photo-induced decomposition was thermally enhanced by exposing

The UV degradation also exists at interfaces; the photocatalytic 5

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encapsulated MAPbI3 devices to 100 suns for 60 min at high tempera­ tures. Changing temperature also causes some structural phase transi­ tions in perovskites [51]. Brunetti et al. [52] studied the thermal and thermodynamic stability of MAPbX3(X ¼ I, Cl or Br) perovskites in an inert atmosphere and found that all these perovskites decomposed to solid lead (II) halide, gaseous methylamine and hydrogen halide at even moderate temperatures like 60 � C. The perovskite was also observed to decompose to elemental lead and iodine and might form PbI2 agglom­ eration on FTO surface under inert conditions at high temperatures whereas the elemental species diffused from perovskite to HTL in the air [54]. It is also well known that the perovskite layer is very sensitive to moisture because its alkylammonium salts are hygoscopic [55]; the intrinsic stability of the perovskite material was reported to be more deterministic than the morphology of the films in air under high relative humidity (RH) [56]. The band gap of the perovskite could also change with high RH exposure and Leguy et al. attributed this degradation to the transformation of the perovskite to monohydrate and further expo­ sure is reported to form PbI2 (Equation (4) and Equation (5)) [57]. Although the monohydrate phase could return to its initial structure at dry atmosphere, the reaction became irreversible upon longer humidity exposure.

explanation for this unexpected result at this stage; however, its simi­ larity with the surprizing light-moisture effect presented in Fig. 4b is notable. We also checked if the ambient conditions affect different cell structures differently. Both regular and inverted cells showed similar degradation behaviors in darkness and under room-light conditions with zero humidity (Fig. 5a and c). We could not make the same comparison for the test performed under strong illumination due to insufficient data. The average degradation pathways of mesoporous regular cells were found to be slightly slower than planar cells both in dark and under room light (Fig. 5b and d). This result is in agreement with the work by Gagliardi and Abate in which the mesoporous architectures were found to be intrinsically more tolerant than planar architectures toward ion vacancy migration, and the stability was reported to be linked to the concentration of ionic defects [59]. However, we could not observe this effect under illumination (Fig. 5e); on the contrary, the planar cells were found to be more stable under illumination conditions (we could not compare regular and inverted structure under illumination because we did not have sufficient number of inverted data). Finally, we compared the degradation behaviors of the cells under different humidity levels in darkness and at room temperature. As seen in Fig. 6a, the degradation of different cell structures (n-i-p/p-i-n) was again found to be very similar under low humidity conditions (0–30% RH) as in the zero humidity (Fig. 5a). However, in Fig. 6b, we found that planar structure degraded slower than mesoporous structure under low humidity conditions as opposite to no humidity conditions; this is not an expected result because it is not only conflicting with no humidity conditions, but it is also different from the result obtained under high humidity (Fig. 6c and e). Indeed, the negative effect of humidity became more significant under higher humidity conditions (30–60% RH and above 60% RH), and the cells having p-i-n structure seem to degrade faster (Fig. 6c and e). We also compared the mesoporous (including nanostructures) and planar architectures for regular cells under 30–60% RH (Fig. 6d); we have found that the average degradation of mesoporous cells were slower as similar to no humidity but opposite 0–30% RH conditions (the data was not sufficient to make this comparison under humidity level above 60% RH). Fakharuddin et al. [60] related the longer durability of mesoporous cells, as we observed under 30–60% RH, to their interface morphology (larger exposure area of the planar devices to ambient atmosphere where perovskite interacted with mois­ ture faster); we have no explanation for the opposite result we obtain under 0–30%RH (Fig. 6b). We did not have sufficient data to do such analysis under room light.

4(CH3NH3)PbI3þH2O ⇌ 4[CH3NH3PbI3.H2O] ⇌ (CH3NH3)4PbI6.2H2O þ 3PbI2 þ 2H2O (4) H2 OðlÞ

ðCH3 NH3 Þ4 PbI6 ⋅ 2H2 OðsÞ�����������!4CH3 NH3 IðaqÞ þ PbI2 ðsÞ þ 2H2 OðlÞ

(5)

Effect of humidity was also analyzed for the cells stored in dark to eliminate the confounding effect of the light; we also fixed the temper­ ature to the room conditions (temperature � 30 � C). According to Fig. 3c and Fig. S3, the effects of humidity was quite clear; the cells stored under zero or lower than 30% RH behaved almost the same and degraded slower than the cells stored under higher humidity conditions. The cells kept at 30–60% and above 60% had the similar degradation patterns indicating that the effects of humidity on the cell stability become sig­ nificant after certain level and do not change with further increase above that level. This result is in an agreement with the study of Noh et al. [58]; they observed significant decay in performance of the most common MAPbI3 based cells after exposure to RH of 55% although the cell has not showed significant degradation of PCE at lower humidity values (~35%). The confounding effects of different factors discussed individually above were also investigated. For example, we checked the humidity effect under different storing conditions (room light and illumination) and presented our results for regular (n-i-p) structure in Fig. 4a and b. The effect of humidity under room light (Fig. 4a) was found to be similar to that in darkness (Fig. 3c) for regular cells; the lower humidity levels resulted more stable cells. Similar patterns were also observed for inverted cells (Fig. S4). However, the results were quite different for different humidity levels under strong illumination (Fig. 4b); the cells seem to be degraded faster under zero humidity. Although these results are somehow in agreement with the studies claiming that photo­ degradation was much more dominant than degradation caused by humidity [48], there may be some other reasons of this pattern that are not clear at this stage. We could not find any significant trend for the effect of oxygen in our data set. The joint effect of temperature and illumination was also investi­ gated (Fig. 4c). However, the temperature rise did not increase the degradation if the artificial illumination was also used even though the photoinduced decomposition was found to be thermally enhanced in literature. For example, Misra et al. [53] exposed encapsulated MAPbI3 devices to illumination at different temperatures; the cell stored at 45–55 � C degraded and caused PbI2 crystallization while the cell stored at 25 � C hasn’t showed any degradation. We have no plausible

3.2. Association rule mining for effects of cell manufacturing materials and procedures In this part, we presented our analysis on long-term stability of perovskite solar cells manufactured using different materials and pro­ cedures. We used number of days passed for a cell to reach to 80% of its initial PCE as the stability criterion. We performed the association rule mining analysis for three periods leading three stability criteria: stable more than 15 days, stable more than 30 days and stable more than 60 days; these criteria were defined cumulatively (for example, more than 15 days class also covers 30 and 60 days data); the analyses for 15 and 30 days were used to follow the trends in time and obtain some additional evi­ dences to back up the analysis for 60 days, which was the longest practical period that we could analyze. We presented the lift versus time plots in Fig. 7 for regular (n-i-p) cells while the results presented and discussed in Supplementary Data (Section S3) for inverted cells; the x-axis represents the days (as 15, 30 and 60) for stable operation whereas the y-axis shows the lift values for individual factors. As it is given in Material and Methods section, the lift is a measure of appearance of a factor in high stability cells relative to entire data set. If the lift of a factor is greater than one, this factor ap­ pears among the stable cells more frequently than that in overall 6

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Fig. 7. Change of lift with storage time for various cell manufacturing factors in association rule mining analysis for regular (n-i-p) type cells. (a) perovskite type, (b) deposition procedure, (c) deposition method, (d) precursor solution, (e) anti-solvent treatment, (f) ETL, (g) ETL-2, (h) HTL, (i) HTL additive, (j) back contact. Numbers next to symbols represents number of cells obeying rule (number in parenthesis for cell with PCE�10%).

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database. For example, in Fig. 7a, the number of mixed cation cells stable more than 60 days is nine, the fraction of the mixed cation cells in stable cells (confidence) is 9=25 ¼ 0:36, whereas the fraction of mixed cation perovskites in overall database is 44=211 ¼ 0:21. Hence, the lift is 0:36=0:21 ¼ 1:71, which indicates that mixed cation perovskite cells favor high stability because their fraction in stable cells are 1.71 times higher than their fraction in entire dataset (in another words, the prob­ ability of having stable mixed cation perovskite cell for more than 60 days has 1.71 times higher than the probability of finding a mixed cation cell in entire database). Since the cases in database were extracted from the literature randomly, these lift values should reflect the trends of the literature well. The size and the adjacent numbers in figure show the number of cases fulfilling the stability requirement in x-axis. The numbers in parenthesis are for the cells with the initial PCE of more than 10% to see the trend for both efficient and stable cells. As Fig. 7a shows, the most stable cells were found to be made of mixed cation perovskites (mostly with MA-FA cations, as the most common combination, together with few Cs-MA-FA and 2D-3D combi­ nations); the lift increases with the increasing time period (1.42, 1.58 and 1.73 for 15, 30 and 60 days, respectively) as a clear indicator of stability. This result is also consistent with the literature that the combining the advantages of different cations is one of the most suc­ cessful and practical methods to obtain desired perovskite structures [61]. This can be also understood by analyzing Goldschmidt tolerance factor (t), which is an indicator of the stability of the crystal structure; it should be between 0.81 and 1.11 at room temperature for a stable perovskite while t between 0.9 and 1 represents the ideal cubic structure [62]. The tolerance factor can be optimized by adjusting the components in perovskite to obtain a stable cubic perovskite. For example, FAPbI3 has tolerance factor greater than one, which causes unwanted yellow phase formation whereas the tolerance factor of CsPbI3 was found to be smaller than one. However, a tolerance factor between 0.9 and 1 could be obtained by using Cs and FA cations together. The use of ternary cations (such as MA, FA and Cs) can also optimize the tolerance factor and suppress unwanted yellow phase formation; MA and Cs act as a black phase stabilizer of FA as well as adjusting tolerance factor towards a cubic structure [26]. As the defects on perovskite surface causes degradation faster [62,63], the suppressed yellow phase formation due to phase stabilization of perovskite leads more uniform and defect-free perovskite morphology, which is needed for high stability. The super alkali-oxide perovskite was also reported to lead suitable tolerance factors [64]. Mixing of 2D and 3D perovskites to combine good optoelectronic properties of 3D perovskites with the robust structure of 2D perovskites also seems to be highly effective to improve the stability. Indeed, many researchers turned towards to study 3D/2D multidimensional perov­ skites and many publications were published in a very short time reporting high efficiency cells with a stable structure including some recent review articles [27,65–67]. A general structure of 2D/3D pe­ rovskites is M2An-1BnX3nþ1 where M is a 2D cation (such as BA, AVA, PEA ¼ phenylethylammonium, PEI ¼ polyethylenimine), A is a 3D cation (such as MA, FA, Cs), B is Pb or Sn, X is a halide anion (such as Cl, I, Br) and n is the number of layers metal halide sheets [68]. The hy­ drophobicity of alkylammonium cations prevents moisture infusion, which causes degradation. However, their wide optical bandgaps are not suitable for sufficient light absorption and charge transfer is reduced because of the long organic spacing-ligand [68]. Hence, combining 3D with 2D perovskites enables robust perovskite structures (because of 2D) with higher charge transfer rates (because of 3D). For example, Snaith et al. reported that including butyl ammonium (BA) cation in 3D perovskite passivated surface traps, increased crystallinity, improved performance and stability [69]. As mentioned in Introduction, Grancini et al. also achieved one-year stable 2D/3D junction perovskite cell using a protonated salt of aminovaleric acid iodide (AVAI) [5]. There are also works that involve 2D materials in HTL or ETL to increase stability [70, 71].

The morphology and crystallinity of the perovskite film, which were highly dependent on the deposition procedures, play an important role on both performance and stability [28,30,62]. One-step procedure ap­ pears to be slightly more stable in Fig. 7b (the difference diminishes at 60 days); however, a more significant difference occurs in the selection of specific deposition techniques as it is given in Fig. 7c. Most of the stable cells, produced using one-step procedure, were made by multiple spinning (spin 2-3) of the perovskite solution. The solvent used during perovskite deposition were also stated to affect the stability through morphology [72]. Indeed, the DMF þ DMSO mixture (themselves or together with some additives such as benzoquinone [73]) improves stability significantly (Fig. 7d). The use of anti-solvent also seems to affect the stability of the cell (Fig. 7e). The chlorobenzene, especially with some additives (such as acetonitrile [74] or some p-type polymers [75]) and diethyl ether (only for 15 days data), seems to improve sta­ bility as it was reported and attributed to the improving the crystalli­ zation and morphology [44,76], while the toluene was found to decrease stability even though the numbers of cases are rather small to be conclusive. Only SnO2 appears as a more stable alternative to the commonly used TiO2 as the ETL compact layers for the 15 and 30 days data (Fig. 7f); no data was available for more than 60 days (probably due to the less frequent use of this material). Jiang et al. [77] also reported that SnO2 was much more stable than TiO2 under ambient conditions and illumi­ nation. Indeed, TiO2 was considered as unstable due to light-induced desorption of surface-adsorbed oxygen [46] even though it is the most common ETL material. In contrast, SnO2 was believed to have lower chemical and photocatalytic reactivity in addition to its wide band gap and high electron mobility; it was also reported to be less hygroscopic than TiO2 [78]. Consequently, an improved stability with this oxide was observed in a significant number of studies [79–82]. Our analysis was not conclusive for ZnO due to the small number of cases. However, Dkhissi et al. found that the stability of MAPbI3 perovskite was lower on ZnO than on TiO2 layer under ambient conditions. The reason was attributed to the heat treatment of perovskite; it was observed that MAPbI3 was degraded rapidly on ZnO layer whereas no degradation was observed on TiO2 layer [83]. Even though no significant difference was observed between the degradation patterns of cells without second ETL layer (planar) and with mesoporous TiO2 as the most definitive trend in Fig. 7g, the use of other ETL alternatives seems to have positive impact on stability supporting the individual works devoted to investigate ETL effects. For example, Fakharuddin et al. employed planar and nanorod TiO2, and they were found that, although planar cells gave higher initial efficiency, the use of nanorod TiO2 scaffold provided longer term durability [60]; the reason was attributed to interface morphology (larger exposure area of the planar devices to ambient atmosphere where perovskite interacted with moisture faster). They also concluded that the stability of the cells was affected by the morphology, porosity and chemical stability of the electron transport layer [60]. Doping of TiO2 was found to enhance the stability by reducing oxygen induced defects, increasing electron transport and reducing charge recombination as it was suggested in some works in literature [84,85]. The positive effect of PCBM layer between the metal oxide layer and perovskite was attributed to the in­ crease of electron transfer and suppression of interface recombination [26]. Although they were not studied sufficient number of time to draw a definitive trend, some other ETL interlayer materials (like [6,6]-phe­ nyl-C61-butyric acid (PCBA) [86], passivated tin oxide (PTO) [87] and mesostructured ZnO [88]) seem to enhance the stability. We found that HTL-free cells and cells with inorganic HTLs were more stable than commonly used spiro-OMeTAD and PTAA even though their numbers in Fig. 7h are low. HTL-free cells not only have simpler structures but they may be also decreasing the probability of negative effect of (organic) HTL materials on stability; for example, the spiroOMeTAD, was found to play role in degradation of the cell at high temperatures [89]. However, it should be noted that three HTL-free cells 8

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stable more than 60 days (and four out of five cells stable more than 30 days) in Fig. 7h also have carbon back contact confounding the effects of these two variables (we do not know which one is contributing more to the stability of the cell). Inorganic HTLs such as CuI [90] and CuSCN [91] were also employed to eliminate the disadvantages of organic HTL compounds as they are chemically and thermally more stable. The most common HTL dopant, Li-TFSI, oxidizes HTL material in the presence of light and air which also causes degradation [92]; Lee et al. [93] attributed this behavior to the hygroscopic nature of Li-TFSI. A chemical interaction between oxidized HTL and TBP was also found to cause degradation [94]. However, LiTFSI þ TBP þ FK209 combination (Fig. 7i) resulted in more stable cells indicating the strong positive ef­ fects of FK209 use as dopant. The cells without HTL additives were also found to be more stable than those with LiTFSI þ TBP alone (which is the most common additive). F4CQN addition also appears to increase sta­ bility as consistent with the results reported in literature [95]. The cells with carbon back contact appear to be more stable (Fig. 7j). However, as mentioned above, three of four cells stable more than 60 days are also HTL-free (four out of five for 30 days); hence it is not clear whether this effect comes from the carbon back contact or HTL-free structure. The silver back contact based cells were detected to be less stable probably due to formation of silver iodide as a result of the re­ action with iodine in perovskite [96]. Similar results were also obtained for inverted (p-i-n) cells as they presented in Supplementary Data (Fig. S5); the positive effect of some specific materials such as mixed cation perovskites, PTAA and NiOx as HTL, PCBM þ C60 as ETL, and BCP interlayer on stability were found for inverted cells. The moisture, oxygen, temperature and light also affect the device stability as it was also discussed in previous section; different decom­ position pathways might occur under different sets of ambient condi­ tions [54,97]. However, the detailed analysis of cell kept in extreme conditions (under illumination, at high temperatures or in special encapsulation) was not possible due to the limited number works. At the end, we could only analyze the effect of humidity in details using as­ sociation rule mining; its effects on cell stability were quite conclusive. The storage of the cells under low humidity conditions (RH of 0–30%) clearly enables longer cell lifetime as the lift value changes from 1.36 in 15 days to 1.17 in 60 days (27% of the cells in this groups actually tested under zero humidity condition). On the other hand, the lift was changed from 0.75 to 0.49 and 0.37 to 0.27 under RH of 30–60% and above 60% respectively as the clear indicator of negative impact of high humidity conditions.

the letter at the top of the node simply denotes the class having the highest fraction in that node. The percentage written inside the first node is 100% because it is the root node (including all data) and the fractions of classes are equal (not processed yet). As the tree is split from root node to down, the percentages inside the nodes decreases (in the branches) and the fraction of one class should increases more until the terminal node (purification). To have a reliable rule or heuristics, the number of cases in a terminal node should be sufficiently large and the purity of the node (i.e. fraction of dominant class) should be as high as possible. As presented in Table S2, the accuracy of the decision tree (fraction of correctly classified cells) was found to be 78% and the precision of Class A, which we are interested in, is 83% (fraction of real class A cells in the group that classified as class A); with these statistics; the tree can be considered as successful. However, there are also considerable amount of data with the PCE of less than 10%, and they may still contain useful information for high stability (and the reliability increases with the data size). Hence we also performed decision tree analysis for the entire data set and presented the results in Fig. S6 and Table S3). As presented in Fig. 8 (for PCE�10% cells), the first split was made upon the storage humidity level; then, the cells stored at low humidity conditions (on the left) were divided again based on HTL additive type (Node 2) creating the first terminal node implying that if F4TCNQ was used as a HTL additive or no HTL additive was used, and the humidity is low, the cell will be stable with 89% probability. It should be noted that there is only one article with F4TCNQ additive in the database satisfying these condition but appeared as statistically significant in the tree due to random sampling; hence, this result should be treated cautiously even though F4TCNQ was also reported to enhance stability significantly in literature [95]. However, the results for the positive effect of additive free HTL on stability (with the other conditions met for Node 2) can be safely generalized because the data were extracted from significant number of articles (five). Similarly, if the branches leading to Node 8 (created based on humidity level, HTL additive type and ETL type), and Node 14 (based on humidity, perovskite type and HTL additive) are followed, Class A cells will be obtained with high probabilities. Although, we usually seek for the conditions to improve the performance criteria, one may also want to follow Class C with the same manner to decide what to avoid. For example, as we investigate the rules for Node 7, the humidity above 60% was found to be detrimental for high stability as 83% of the cases with this humidity conditions indeed have low stability as ex­ pected; similarly, the other nodes with red color could be also used to see the unfavorable conditions for the stability.

3.3. Decision trees analysis for rules and heuristics for stability

3.4. Factors identified by the analyses to improve stability

Decision tree classification was used to see if it is possible to obtain a set of rules describing conditions leading highly stable cells; for this purpose, the regular cell data were divided into three classes: cells preserved 80% of initial efficiency more than 60 days (Class A), cells preserved 80% of initial efficiency within 7–60 days (Class B) and cells preserved 80% of initial efficiency less than seven days (Class C). We could not perform this analysis for inverted structure due to insufficient number of data points for Class A. We constructed a decision tree for stable regular cells, which are also have the initial PCE�10% so that we can develop rules and heuristics (if there are any) for the cells that are both sufficiently efficient and stable (Fig. 8). The colors in tree represent the majority of the classes (Class A: green, Class B: grey and Class C: red) while the percentage at the bottom of each node indicates the fraction of total data obeying rules that are used up to that node. The fractions in the middle line of the boxes shows the fraction of classes A, B and C from left to right respectively; finally,

We summarized the most effective factors that we found for high stability for regular and inverted cells in Table 1. For regular cells, the basic exploratory data analysis confirmed the negative effects of illu­ mination, high humidity and temperature as well as different stability behavior of different cell structures under various conditions as dis­ cussed in Section 3.1; planar regular cells were found to be less stable than mesoporous regular cells in general as presented in Supplementary Data. Association rule mining results revealed that mixed cation pe­ rovskites, multi-spin coating method (for perovskite) as one-step depo­ sition, DMF þ DMSO as precursor solution and chlorobenzene as antisolvent were found to have positive effects on high stability (Section 3.2). Additionally, SnO2 as ETL compact layer, PCBM as second ETL, inorganic HTLs or HTL-free cells, LiTFSI þ TBP þ FK209 and F4TCNQ as HTL additives and carbon as back contact were found to be the other factors improving stability. Storing cells at low humidity was also found to be significant for high stability as expected. The decision tree results

9

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Solar Energy Materials and Solar Cells xxx (xxxx) xxx

Fig. 8. Decision tree model for the stability of regular (n-i-p) cells with PCE�10% (minimum split number ¼ 5, maximum depth ¼ 6, complexity parameter ¼ 0).

As we discussed above the use of 2D/3D perovskites is another strategy that employed frequently in recent works to obtain stable cells with reasonably high efficiencies even though our work could not identify that results due to insufficient data. Additionally, the encapsu­ lation, as well as the use of some protective layers are also studied quite often to protect cells from environmental conditions and these solutions were found to be very promising for commercialization [98].

Table 1 Materials and methods leading high stability for regular and inverted cells. Material/method

Regular (n-i-p) cells

Inverted (p-i-n) cells

Perovskite

Mixed cation

Perovskite Deposition Solvent

One-step: spin 2-3

Mixed cation (also MAPbI3-xClx) Spin-spin

Anti-solvent ETL ETL second layer/ interlayer HTL HTL additive Back contact

DMF þ DMSO þ othersa) (also DMF þ DMSO) Chlorobenzene þ othersb) (also chlorobenzene & diethyl ether) SnO2 PCBM (also doped-mTiO2) Inorganic HTLS (and HTL-free) F4TCNQ (also Li þ TBP þ FK209 & no HTL additive) Carbon

DMF þ othersc)

4. Conclusions

Without anti-solvent (also chlorobenzene) PCBM þ C60 BCP

In summary, we analyzed the long-term stability of perovskite solar cells by using the data extracted from past publications. First, we analyzed the effect of ambient conditions on stability using descriptive plots. Then, the effect of cell related factors (types of perovskite, ETL, HTL, back contact, deposition details etc.) were analyzed using associ­ ation rule mining. Finally, a decision tree was built for the possibility to deduce rules and heuristics that can help to fabricate stable cells. The materials and methods that lead more stable cells were sum­ marized in Table 1 while the negative effects of high humidity, high temperature and high illumination were also verified in our analyses (Section 3). Additionally, 2D/3D perovskites are studied extensively in recent works to enhance the stability while there are also significant number evidences in literature shoving the positive effects of encapsulation. This work also revealed that this type of analyses may be beneficial to review and understand the literature better, see the overall picture and draw some valuable conclusions for high stability. However, two important points should be always kept in mind. First, these methods were designed to draw generalizable conclusions from the large data sets, and in some sense, convert the experiences of large groups into comprehensible knowledge. Hence, by their nature, they may not cap­ ture the emerging and promising alternatives due to their insufficient number of data points or lower initial (immature) performances against the more established competitors; hence, new alternatives should be always treated individually. Second, unfortunately, the standard testing

PTAA (also NiOx) –

Cu, Al

a)

others include benzoquinone (BQ) for 60 days, 2-pyridylthiourea, NMethyl-2-Pyrrolidone (NMP), Pb(SCN)2 for 30 days. b) others include acetonitrile for 60 days, toluene, p-type polymer with or without (w/wo) molecular fluorination (PF-0, PF-1), n-type polymer w/wo molecular fluorination (N2200, F–N2200) for 30 days. c) others include N-cyclohexyl-2-pyrrolidone (CHP) and graphene oxide (GO).

verified the results obtained in association rule mining. We found that the degradation was slightly faster in presence of humidity for inverted cells (presented in Supplementary Material). In association rule mining analysis, the mixed cation perovskites, two-step spin coating (for perovskite), employing DMF with some novel additives (such as N-cyclohexyl-2-pyrrolidone or GO) as perovskite precursor so­ lution, not use of anti-solvent, using PTAA and NiOx as HTL, PCBM and C60 together as ETL and BCP as ETL interlayer were also found to have a positive effect on high stability. 10

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and reporting protocols are not yet established for the stability studies of the perovskite solar cells (this is also true for efficiency measurements). This significantly limits the use of machine learning techniques because nonstandard measurements and storage conditions, and lack of clarity and completeness in reporting increase the variability in data. We can expect to have more reliable results in these type analyses when the standard testing, storage and reporting conditions are established.

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