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

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

Afiliación

Mammeri M; LEPCM, Faculty of Science, University of Batna 1, Algeria.
Dehimi L; LEPCM, Faculty of Science, University of Batna 1, Algeria.
Bencherif H; LEREESI, HNS-RE2SD, Higher National School of Renewable Energy, Environment and Sustainable Development, Batna, 05078, Algeria.
Amami M; Department of Chemistry, College of Sciences, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia.
Ezzine S; Department of Chemistry, College of Sciences, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia.
Pandey R; VLSI Centre of Excellence, Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, 140401, India.
Hossain MK; Institute of Electronics, Atomic Energy Research Establishment, Bangladesh Atomic Energy Commission, Dhaka, 1349, Bangladesh.

Heliyon ; 9(11): e21498, 2023 Nov.

Article en En | MEDLINE | ID: mdl-37964826

Palabras clave

Machine learning; Optimization; Perovskite solar cell; Prediction; Random forest technique

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2023 Tipo del documento: Article País de afiliación: Argelia Pais de publicación: Reino Unido

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