dor_id: 4150015

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

561.#.#.u: https://www.icat.unam.mx/

650.#.4.x: Ingenierías

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/2057/1042

100.1.#.a: Velez Sanchez, Jeisson Emilio; Botero Londoño, Monica Andrea; Sepulveda Sepulveda, Franklin Alexander; Otalora Bastidas, Camilo Andres; Camacho Parra, Cristian David

524.#.#.a: Velez Sanchez, Jeisson Emilio, et al. (2023). Absorber Layer Thickness as a New Feature in Statistical Learning Tools of Perovskite Solar Cells. Journal of Applied Research and Technology; Vol. 21 Núm. 5, 2023; 858-865. Recuperado de https://repositorio.unam.mx/contenidos/4150015

245.1.0.a: Absorber Layer Thickness as a New Feature in Statistical Learning Tools of Perovskite Solar Cells

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2023

264.#.1.c: 2023-10-30

653.#.#.a: Perovskite solar cells; machine learning; mutual information; thickness

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/2057

001.#.#.#: 074.oai:ojs2.localhost:article/2057

041.#.7.h: eng

520.3.#.a: In the last decade, the development of perovskite-based solar cells has emerged as a technological alternative for the photovoltaic generation with a higher efficiency/cost ratio. Many contributions have been made in recent years, as evidenced by many academic publications with worldwide experimental results in this area. Machine learning as a tool can support the development of this technology by predicting new materials, novel solar cell configurations, and evaluating the most relevant experimental parameters, among others. For this, the automatic learning models used in predicting or classifying the information available in the literature or generated experimentally must be improved. One way to improve these models is by including new descriptors that allow improving the prediction. In this work, we evaluated the use of the absorber layer thickness as a descriptor in a linear regression model using a database of 221 literature records containing information on the bandgap, the ?HOMO (perovskite-HTL), and ?LUMO (perovskite-ETL) of different perovskite cells, together with the thickness of the absorber layer. By building two multiple linear regression models, including or not the thickness of the absorber layer, a reduction in the root mean square error RMSE of 4.4% and 2.8% was found in the prediction of the Jsc and PCE, respectively. By applying a linear regression model, an improvement in the prediction of Jsc can be seen due to the inclusion of thickness as a descriptor, which is in line with the relatively high value of the mutual information measure between thickness and Jsc.

773.1.#.t: Journal of Applied Research and Technology; Vol. 21 Núm. 5 (2023); 858-865

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 858-865

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2023.21.5.2057

harvesting_date: 2023-11-08 13:10:00.0

856.#.0.q: application/pdf

file_creation_date: 2023-10-24 22:14:04.0

file_modification_date: 2023-10-24 22:14:09.0

file_creator: Yolanda G.G.

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last_modified: 2024-03-19 14:00:00

license_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es

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