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

Literature Information

Publication Date 2024-01-03
DOI 10.1039/D3TA05966A
Impact Factor 12.732
Authors

Bashayer Nafe N. Alsulami, Tudur Wyn David, A. Essien, T. Jesper Jacobsson, Andrew Feeney, Jeff Kettle


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Abstract

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.

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Source Journal

Journal of Materials Chemistry A

Journal of Materials Chemistry A
CiteScore: 19.5
Self-citation Rate: 4.7%
Articles per Year: 2211

Journal of Materials Chemistry A, B & C cover high quality studies across all fields of materials chemistry. The journals focus on those theoretical or experimental studies that report new understanding, applications, properties and synthesis of materials. The journals have a strong history of publishing quality reports of interest to interdisciplinary communities and providing an efficient and rigorous service through peer review and publication. The journals are led by an international team of Editors-in-Chief and Associate Editors who are all active researchers in their fields. Journal of Materials Chemistry A, B & C are separated by the intended application of the material studied. Broadly, applications in energy and sustainability are of interest to Journal of Materials Chemistry A, applications in biology and medicine are of interest to Journal of Materials Chemistry B, and applications in optical, magnetic and electronic devices are of interest to Journal of Materials Chemistry C. More than one Journal of Materials Chemistry journal may be suitable for certain fields and researchers are encouraged to submit their paper to the journal that they feel best fits for their particular article. Example topic areas within the scope of Journal of Materials Chemistry A are listed below. This list is neither exhaustive nor exclusive. Artificial photosynthesis Batteries Carbon dioxide conversion Catalysis Fuel cells Gas capture/separation/storage Green/sustainable materials Hydrogen generation Hydrogen storage Photocatalysis Photovoltaics Self-cleaning materials Self-healing materials Sensors Supercapacitors Thermoelectrics Water splitting Water treatment

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