Enhancing the stability of Organic Photovoltaics through Machine Learning

A machine learning approach for extracting information from organic photovoltaic (OPV) solar cell data is presented. A database consisting of 1850 entries of device characteristics, performance and stability data is utilised and a sequential minimal optimisation regression (SMOreg) model is employed as a means of determining the most influential factors governing the solar cell stability and power conversion efficiency (PCE). This is achieved through the analysis of the acquired SMOreg model in terms of the attribute weights. Significantly, the analysis presented allows for identification of materials which could lead to improvements in stability and PCE for each thin film in the device architecture, as well as highlighting the role of different stress factors in the degradation of OPVs. It is found that, for tests conducted under ISOS-L protocols the choice of light spectrum and the active layer material significantly govern the stability, whilst for tests conducted under ISOS-D protocols, the primary attributes are material and encapsulation dependent. The reported approach affords a rapid and efficient method of applying machine learning to enable material identification that possess the best stability and performance. Ultimately, researchers and industries will be able to obtain invaluable information for developing future OPV technologies so that can be realised in a significantly shorter period by reducing the need for time-consuming experimentation and optimisation

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

Properties of Contact and Bulk Impedances in Hybrid Lead Halide Perovskite Solar Cells Including Inductive Loop Elements

Impedance spectroscopy offers access to all the different electronic and ionic processes taking place simultaneously in an operating solar cell. To date, its use on perovskite solar cells has been challenging because of the richness of the physical processes occurring within similar time domains. The aim of this work is to understand the general impedance response and propose a general equivalent circuit model that accounts for the different processes and gives access to quantitative analysis. When the electron-selective contacts and the thickness of the perovskite film are systematically modified, it is possible to distinguish between the characteristic impedance signals of the perovskite layer and those arising from the contacts. The study is carried out using mixed organic lead halogen perovskite (FA(0.85)MA(0.15)Pb(I0.85Br0.15)(3)) solar cells with three different electron-selective contacts: SnO2, TiO2, and Nb2O5. The contacts have been deposited by atomic layer deposition (ALD), which provides pinhole-free films and excellent thickness control in the absence of a mesoporous layer to simplify the impedance analysis. It was found that the interfacial impedance has a rich structure that reveals different capacitive processes, serial steps for electron extraction, and a prominent inductive loop related to negative capacitance at intermediate frequencies. Overall, the present report provides insights into the impedance response of perovskite solar cells which enable an understanding of the different electronic and ionic processes taking place during device operation.The work at INAM-UJI was supported by Generalitat Valenciana project PROMETEO/2014/020 and MINECO of Spain under Project MAT2013-47192-C3-1-R. A.G. thanks the Spanish Ministerio de Economía y Competitividad for a Ramón y Cajal Fellowship (RYC-2014-16809). T.J.J. gratefully acknowledges the GRAPHENE project supported by the European Commission Seventh Framework Program under Contract 604391

Application of large datasets to assess trends in the stability of perovskite photovoltaics through machine learning

Current trends in manufacturing indicate that optimised decision making using new state-of-the-art machine learning (ML) technologies will be used. ML is a versatile technique that rapidly and accurately generates new insights from multifactorial data. The ML approach has been applied to a perovskite solar cell (PSC) database to elucidate trends in stability and forecast the stability of new configurations. A database consisting of 6038 entries of device characteristics, performance, and stability data was utilised, and a sequential minimal optimisation regression (SMOreg) model was employed to determine the most influential factors governing solar cell stability. When considering sub-sections of data, it was found that pin-device architectures provided the best model fittings with a training correlation efficiency of 0.963, compared to 0.699 for all device architectures. By establishing models for each PSC architecture, the analysis allows the identification of materials that can lead to improvements in stability. This paper also attempts to summarise some key challenges and trends in the current research methodologies

Device Performance of Emerging Photovoltaic Materials (Version 3)

Following the 2nd release of the “Emerging PV reports,” the best achievements in the performance of emerging photovoltaic devices in diverse emerging photovoltaic research subjects are summarized, as reported in peer-reviewed articles in academic journals since August 2021. Updated graphs, tables, and analyses are provided with several performance parameters, e.g., power conversion efficiency, open-circuit voltage, short-circuit current density, fill factor, light utilization efficiency, and stability test energy yield. These parameters are presented as a function of the photovoltaic bandgap energy and the average visible transmittance for each technology and application, and are put into perspective using, e.g., the detailed balance efficiency limit. The 3rd installment of the “Emerging PV reports” extends the scope toward triple junction solar cells

Device Performance of Emerging Photovoltaic Materials (Version 3)

Following the 2nd release of the “Emerging PV reports,” the best achievements in the performance of emerging photovoltaic devices in diverse emerging photovoltaic research subjects are summarized, as reported in peer-reviewed articles in academic journals since August 2021. Updated graphs, tables, and analyses are provided with several performance parameters, e.g., power conversion efficiency, open-circuit voltage, short-circuit current density, fill factor, light utilization efficiency, and stability test energy yield. These parameters are presented as a function of the photovoltaic bandgap energy and the average visible transmittance for each technology and application, and are put into perspective using, e.g., the detailed balance efficiency limit. The 3rd installment of the “Emerging PV reports” extends the scope toward triple junction solar cells

Device Performance of Emerging Photovoltaic Materials (Version 2)

Following the 1st release of the “Emerging photovoltaic (PV) reports”, the best achievements in the performance of emerging photovoltaic devices in diverse emerging photovoltaic research subjects are summarized, as reported in peer-reviewed articles in academic journals since August 2020. Updated graphs, tables, and analyses are provided with several performance parameters, e.g., power conversion efficiency, open-circuit voltage, short-circuit current density, fill factor, light utilization efficiency, and stability test energy yield. These parameters are presented as a function of the photovoltaic bandgap energy and the average visible transmittance for each technology and application and are put into perspective using, e.g., the detailed balance efficiency limit. The 2nd instalment of the “Emerging PV reports” extends the scope toward tandem solar cells and presents the current state-of-the-art in tandem solar cell performance for various material combinations.</p

Device Performance of Emerging Photovoltaic Materials (Version 1)

Emerging photovoltaics (PVs) focus on a variety of applications complementing large scale electricity generation. Organic, dye‐sensitized, and some perovskite solar cells are considered in building integration, greenhouses, wearable, and indoor applications, thereby motivating research on flexible, transparent, semitransparent, and multi‐junction PVs. Nevertheless, it can be very time consuming to find or develop an up‐to‐date overview of the state‐of‐the‐art performance for these systems and applications. Two important resources for recording research cells efficiencies are the National Renewable Energy Laboratory chart and the efficiency tables compiled biannually by Martin Green and colleagues. Both publications provide an effective coverage over the established technologies, bridging research and industry. An alternative approach is proposed here summarizing the best reports in the diverse research subjects for emerging PVs. Best performance parameters are provided as a function of the photovoltaic bandgap energy for each technology and application, and are put into perspective using, e.g., the Shockley–Queisser limit. In all cases, the reported data correspond to published and/or properly described certified results, with enough details provided for prospective data reproduction. Additionally, the stability test energy yield is included as an analysis parameter among state‐of‐the‐art emerging PVs