Targeting high performance of perovskite solar cells by combining electronic, manufacturing and environmental features in machine learning techniques

Mammeri, M.; Dehimi, L.; Bencherif, H.; Amami, Mongi; Ezzine, Safa; Pandey, Rahul; Hossain, M. Khalid

doi:10.1016/j.heliyon.2023.e21498

Heliyon

2023

DOI: 10.1016/j.heliyon.2023.e21498

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Targeting high performance of perovskite solar cells by combining electronic, manufacturing and environmental features in machine learning techniques

M. Mammeri,

L. Dehimi,

H. Bencherif

et al.

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2024

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Cited by 3 publications

References 28 publications

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Machine Learning for Perovskite Solar Cells: An Open‐Source Pipeline

Roberts,

Jones,

Schuy

et al. 2024

Advanced Physics Research

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Among promising applications of metal‐halide perovskite, the most research progress is made for perovskite solar cells (PSCs). Data from myriads of research work enables leveraging machine learning (ML) to significantly expedite material and device optimization as well as potentially design novel configurations. This paper represents one of the first efforts in providing open‐source ML tools developed utilizing the Perovskite Database Project (PDP), the most comprehensive open‐source PSC database to date with over 43 000 entries from published literature. Three ML model architectures with short‐circuit current density (Jsc) as a target are trained exploiting the PDP. Using the XGBoost architecture, a root mean squared error (RMSE) of 3.58 , R2 of 0.35 and a mean absolute percentage error (MAPE) of 9.49% are achieved. This performance is comparable to results reported in literature, and through further investigation can likely be improved. To overcome challenges with manual database creation, an open‐source data cleaning pipeline is created for PDP data. Through the creation of these tools, which have been published on GitHub, this research aims to make ML available to aid the design for PSC while showing the already promising performance achieved. The tools can be adapted for other applications, such as perovskite light‐emitting diodes (PeLEDs), if a sufficient database is available.

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Machine Learning for Perovskite Solar Cells: An Open‐Source Pipeline

Roberts,

Jones,

Schuy

et al. 2024

Advanced Physics Research

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Stability forecasting of perovskite solar cells utilizing various machine learning and deep learning techniques

Mammeri,

Bencherif,

Dehimi

et al. 2024

J Opt

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Numerical interface optimization of lead-free perovskite solar cells (CH3NH3SnI3) for 30% photo-conversion efficiency using SCAPS-1D

Shoab,

Aslam,

Zulfequar

et al. 2024

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Targeting high performance of perovskite solar cells by combining electronic, manufacturing and environmental features in machine learning techniques

Cited by 3 publications

References 28 publications

Machine Learning for Perovskite Solar Cells: An Open‐Source Pipeline

Machine Learning for Perovskite Solar Cells: An Open‐Source Pipeline

Stability forecasting of perovskite solar cells utilizing various machine learning and deep learning techniques

Numerical interface optimization of lead-free perovskite solar cells (CH3NH3SnI3) for 30% photo-conversion efficiency using SCAPS-1D

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