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Utilizing Machine Learning and Diode Physics to Investigate the Effects of Stoichiometry on Photovoltaic Performance in Sequentially Processed Perovskite Solar Cells
[Image: see text] Organic–inorganic metal halide perovskite solar cells are renowned for their extensive solution processability, although the production of uniformly crystalline perovskite films can necessitate intricate deposition methods. In our study, we harmonized Shockley diode-based numerical...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10633957/ https://www.ncbi.nlm.nih.gov/pubmed/37969995 http://dx.doi.org/10.1021/acsomega.3c05622 |
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author | Cha, Jeongbeom Baek, Dohun Jin, Haedam Na, Hyemi Park, Geon Yeong Ham, Dong Seok Kim, Min |
author_facet | Cha, Jeongbeom Baek, Dohun Jin, Haedam Na, Hyemi Park, Geon Yeong Ham, Dong Seok Kim, Min |
author_sort | Cha, Jeongbeom |
collection | PubMed |
description | [Image: see text] Organic–inorganic metal halide perovskite solar cells are renowned for their extensive solution processability, although the production of uniformly crystalline perovskite films can necessitate intricate deposition methods. In our study, we harmonized Shockley diode-based numerical analysis with machine learning techniques to extract the device characteristics of perovskite solar cells and optimize their photovoltaic performance in light of the experimental variables. The application of the Shockley diode equation facilitated the extraction of photovoltaic parameters and the prediction of power conversion efficiencies, thus aiding the understanding of device physics and charge recombination. Through machine learning, specifically Gaussian process regression, we trained models on current–voltage curves sensitive to variations in fabrication conditions, thereby pinpointing the optimal settings for enhanced device performance. Our multifaceted approach not only clarifies the interplay between experimental conditions and device performance but also streamlines the optimization process, diminishing the need for exhaustive trial-and-error experiments. This methodology holds substantial promise for advancing the development and fine-tuning of next-generation perovskite solar cells. |
format | Online Article Text |
id | pubmed-10633957 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-106339572023-11-15 Utilizing Machine Learning and Diode Physics to Investigate the Effects of Stoichiometry on Photovoltaic Performance in Sequentially Processed Perovskite Solar Cells Cha, Jeongbeom Baek, Dohun Jin, Haedam Na, Hyemi Park, Geon Yeong Ham, Dong Seok Kim, Min ACS Omega [Image: see text] Organic–inorganic metal halide perovskite solar cells are renowned for their extensive solution processability, although the production of uniformly crystalline perovskite films can necessitate intricate deposition methods. In our study, we harmonized Shockley diode-based numerical analysis with machine learning techniques to extract the device characteristics of perovskite solar cells and optimize their photovoltaic performance in light of the experimental variables. The application of the Shockley diode equation facilitated the extraction of photovoltaic parameters and the prediction of power conversion efficiencies, thus aiding the understanding of device physics and charge recombination. Through machine learning, specifically Gaussian process regression, we trained models on current–voltage curves sensitive to variations in fabrication conditions, thereby pinpointing the optimal settings for enhanced device performance. Our multifaceted approach not only clarifies the interplay between experimental conditions and device performance but also streamlines the optimization process, diminishing the need for exhaustive trial-and-error experiments. This methodology holds substantial promise for advancing the development and fine-tuning of next-generation perovskite solar cells. American Chemical Society 2023-10-26 /pmc/articles/PMC10633957/ /pubmed/37969995 http://dx.doi.org/10.1021/acsomega.3c05622 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Cha, Jeongbeom Baek, Dohun Jin, Haedam Na, Hyemi Park, Geon Yeong Ham, Dong Seok Kim, Min Utilizing Machine Learning and Diode Physics to Investigate the Effects of Stoichiometry on Photovoltaic Performance in Sequentially Processed Perovskite Solar Cells |
title | Utilizing Machine
Learning and Diode Physics to Investigate
the Effects of Stoichiometry on Photovoltaic Performance in Sequentially
Processed Perovskite Solar Cells |
title_full | Utilizing Machine
Learning and Diode Physics to Investigate
the Effects of Stoichiometry on Photovoltaic Performance in Sequentially
Processed Perovskite Solar Cells |
title_fullStr | Utilizing Machine
Learning and Diode Physics to Investigate
the Effects of Stoichiometry on Photovoltaic Performance in Sequentially
Processed Perovskite Solar Cells |
title_full_unstemmed | Utilizing Machine
Learning and Diode Physics to Investigate
the Effects of Stoichiometry on Photovoltaic Performance in Sequentially
Processed Perovskite Solar Cells |
title_short | Utilizing Machine
Learning and Diode Physics to Investigate
the Effects of Stoichiometry on Photovoltaic Performance in Sequentially
Processed Perovskite Solar Cells |
title_sort | utilizing machine
learning and diode physics to investigate
the effects of stoichiometry on photovoltaic performance in sequentially
processed perovskite solar cells |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10633957/ https://www.ncbi.nlm.nih.gov/pubmed/37969995 http://dx.doi.org/10.1021/acsomega.3c05622 |
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