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Machine Learning for Halide Perovskite Materials ABX(3) (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy

The exact control of material properties essential for solar applications has been made possible because of perovskites’ compositional engineering. However, tackling efficiency, stability, and toxicity at the same time is still a difficulty. Mixed lead-free and inorganic perovskites have lately show...

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Autores principales: Alhashmi, Afnan, Kanoun, Mohammed Benali, Goumri-Said, Souraya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10095675/
https://www.ncbi.nlm.nih.gov/pubmed/37048955
http://dx.doi.org/10.3390/ma16072657
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author Alhashmi, Afnan
Kanoun, Mohammed Benali
Goumri-Said, Souraya
author_facet Alhashmi, Afnan
Kanoun, Mohammed Benali
Goumri-Said, Souraya
author_sort Alhashmi, Afnan
collection PubMed
description The exact control of material properties essential for solar applications has been made possible because of perovskites’ compositional engineering. However, tackling efficiency, stability, and toxicity at the same time is still a difficulty. Mixed lead-free and inorganic perovskites have lately shown promise in addressing these problems, but their composition space is vast, making it challenging to find good candidates even with high-throughput approaches. We investigated two groups of halide perovskite compound data with the ABX(3) formula to investigate the formation energy data for 81 compounds. The structural stability was analyzed over 63 compounds. For these perovskites, we used new library data extracted from a calculation using generalized-gradient approximation within the Perdew–Burke–Ernzerhof (PBE) functional established on density functional theory. As a second step, we built machine learning models, based on a kernel-based naive Bayes algorithm that anticipate a variety of target characteristics, including the mixing enthalpy, different octahedral distortions, and band gap calculations. In addition to laying the groundwork for observing new perovskites that go beyond currently available technical uses, this work creates a framework for finding and optimizing perovskites in a photovoltaic application.
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spelling pubmed-100956752023-04-13 Machine Learning for Halide Perovskite Materials ABX(3) (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy Alhashmi, Afnan Kanoun, Mohammed Benali Goumri-Said, Souraya Materials (Basel) Article The exact control of material properties essential for solar applications has been made possible because of perovskites’ compositional engineering. However, tackling efficiency, stability, and toxicity at the same time is still a difficulty. Mixed lead-free and inorganic perovskites have lately shown promise in addressing these problems, but their composition space is vast, making it challenging to find good candidates even with high-throughput approaches. We investigated two groups of halide perovskite compound data with the ABX(3) formula to investigate the formation energy data for 81 compounds. The structural stability was analyzed over 63 compounds. For these perovskites, we used new library data extracted from a calculation using generalized-gradient approximation within the Perdew–Burke–Ernzerhof (PBE) functional established on density functional theory. As a second step, we built machine learning models, based on a kernel-based naive Bayes algorithm that anticipate a variety of target characteristics, including the mixing enthalpy, different octahedral distortions, and band gap calculations. In addition to laying the groundwork for observing new perovskites that go beyond currently available technical uses, this work creates a framework for finding and optimizing perovskites in a photovoltaic application. MDPI 2023-03-27 /pmc/articles/PMC10095675/ /pubmed/37048955 http://dx.doi.org/10.3390/ma16072657 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alhashmi, Afnan
Kanoun, Mohammed Benali
Goumri-Said, Souraya
Machine Learning for Halide Perovskite Materials ABX(3) (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy
title Machine Learning for Halide Perovskite Materials ABX(3) (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy
title_full Machine Learning for Halide Perovskite Materials ABX(3) (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy
title_fullStr Machine Learning for Halide Perovskite Materials ABX(3) (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy
title_full_unstemmed Machine Learning for Halide Perovskite Materials ABX(3) (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy
title_short Machine Learning for Halide Perovskite Materials ABX(3) (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy
title_sort machine learning for halide perovskite materials abx(3) (b = pb, x = i, br, cl) assessment of structural properties and band gap engineering for solar energy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10095675/
https://www.ncbi.nlm.nih.gov/pubmed/37048955
http://dx.doi.org/10.3390/ma16072657
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