Modeling of lattice parameters of cubic perovskite oxides and halides
Perovskites having the chemical formulae of ABX3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is a (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result o...
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| Published in | Heliyon Vol. 7; no. 7; p. e07601 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
01.07.2021
Elsevier |
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| Abstract | Perovskites having the chemical formulae of ABX3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is a (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result of interactions between different ions, determined by valence electrons and ionic radii. The size and stability of unit cells have important influences on structural stabilities, bandgap structures, and therefore performance of materials. To obtain the lattice constant of cubic perovskites without going through experimental efforts such as synthesis and measurements, we construct a model based on Gaussian process regressions for cubic perovskite lattice constant predictions. The model utilizes the number of valence electrons as well as ionic radii of alloying elements as predictors. A total of 149 cubic perovskites containing fluorides, chlorides, and bromides with cation combinations of A1+B2+, as well as oxides with cation combinations of A1+B5+, A2+B4+, and A3+B3+ are explored. The model demonstrates good performance in terms of stabilities and accuracy, and thus could be a rapid approach to estimate lattice constants.
Perovskite; Crystal structure; Oxide; Halide; Machine learning |
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| AbstractList | Perovskites having the chemical formulae of ABX3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is a (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result of interactions between different ions, determined by valence electrons and ionic radii. The size and stability of unit cells have important influences on structural stabilities, bandgap structures, and therefore performance of materials. To obtain the lattice constant of cubic perovskites without going through experimental efforts such as synthesis and measurements, we construct a model based on Gaussian process regressions for cubic perovskite lattice constant predictions. The model utilizes the number of valence electrons as well as ionic radii of alloying elements as predictors. A total of 149 cubic perovskites containing fluorides, chlorides, and bromides with cation combinations of A1+B2+, as well as oxides with cation combinations of A1+B5+, A2+B4+, and A3+B3+ are explored. The model demonstrates good performance in terms of stabilities and accuracy, and thus could be a rapid approach to estimate lattice constants. Perovskites having the chemical formulae of ABX are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result of interactions between different ions, determined by valence electrons and ionic radii. The size and stability of unit cells have important influences on structural stabilities, bandgap structures, and therefore performance of materials. To obtain the lattice constant of cubic perovskites without going through experimental efforts such as synthesis and measurements, we construct a model based on Gaussian process regressions for cubic perovskite lattice constant predictions. The model utilizes the number of valence electrons as well as ionic radii of alloying elements as predictors. A total of 149 cubic perovskites containing fluorides, chlorides, and bromides with cation combinations of A B , as well as oxides with cation combinations of A B , A B , and A B are explored. The model demonstrates good performance in terms of stabilities and accuracy, and thus could be a rapid approach to estimate lattice constants. Perovskites having the chemical formulae of ABX3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is a (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result of interactions between different ions, determined by valence electrons and ionic radii. The size and stability of unit cells have important influences on structural stabilities, bandgap structures, and therefore performance of materials. To obtain the lattice constant of cubic perovskites without going through experimental efforts such as synthesis and measurements, we construct a model based on Gaussian process regressions for cubic perovskite lattice constant predictions. The model utilizes the number of valence electrons as well as ionic radii of alloying elements as predictors. A total of 149 cubic perovskites containing fluorides, chlorides, and bromides with cation combinations of A1+B2+, as well as oxides with cation combinations of A1+B5+, A2+B4+, and A3+B3+ are explored. The model demonstrates good performance in terms of stabilities and accuracy, and thus could be a rapid approach to estimate lattice constants.Perovskites having the chemical formulae of ABX3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is a (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result of interactions between different ions, determined by valence electrons and ionic radii. The size and stability of unit cells have important influences on structural stabilities, bandgap structures, and therefore performance of materials. To obtain the lattice constant of cubic perovskites without going through experimental efforts such as synthesis and measurements, we construct a model based on Gaussian process regressions for cubic perovskite lattice constant predictions. The model utilizes the number of valence electrons as well as ionic radii of alloying elements as predictors. A total of 149 cubic perovskites containing fluorides, chlorides, and bromides with cation combinations of A1+B2+, as well as oxides with cation combinations of A1+B5+, A2+B4+, and A3+B3+ are explored. The model demonstrates good performance in terms of stabilities and accuracy, and thus could be a rapid approach to estimate lattice constants. Perovskites having the chemical formulae of ABX 3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is a (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result of interactions between different ions, determined by valence electrons and ionic radii. The size and stability of unit cells have important influences on structural stabilities, bandgap structures, and therefore performance of materials. To obtain the lattice constant of cubic perovskites without going through experimental efforts such as synthesis and measurements, we construct a model based on Gaussian process regressions for cubic perovskite lattice constant predictions. The model utilizes the number of valence electrons as well as ionic radii of alloying elements as predictors. A total of 149 cubic perovskites containing fluorides, chlorides, and bromides with cation combinations of A 1+ B 2+ , as well as oxides with cation combinations of A 1+ B 5+ , A 2+ B 4+ , and A 3+ B 3+ are explored. The model demonstrates good performance in terms of stabilities and accuracy, and thus could be a rapid approach to estimate lattice constants. Perovskite; Crystal structure; Oxide; Halide; Machine learning Perovskites having the chemical formulae of ABX₃ are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is a (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result of interactions between different ions, determined by valence electrons and ionic radii. The size and stability of unit cells have important influences on structural stabilities, bandgap structures, and therefore performance of materials. To obtain the lattice constant of cubic perovskites without going through experimental efforts such as synthesis and measurements, we construct a model based on Gaussian process regressions for cubic perovskite lattice constant predictions. The model utilizes the number of valence electrons as well as ionic radii of alloying elements as predictors. A total of 149 cubic perovskites containing fluorides, chlorides, and bromides with cation combinations of A¹⁺B²⁺, as well as oxides with cation combinations of A¹⁺B⁵⁺, A²⁺B⁴⁺, and A³⁺B³⁺ are explored. The model demonstrates good performance in terms of stabilities and accuracy, and thus could be a rapid approach to estimate lattice constants. Perovskites having the chemical formulae of ABX3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important structural factors is a (the lattice constant), which represents the unit cell size. The variation in the lattice constant is a combined result of interactions between different ions, determined by valence electrons and ionic radii. The size and stability of unit cells have important influences on structural stabilities, bandgap structures, and therefore performance of materials. To obtain the lattice constant of cubic perovskites without going through experimental efforts such as synthesis and measurements, we construct a model based on Gaussian process regressions for cubic perovskite lattice constant predictions. The model utilizes the number of valence electrons as well as ionic radii of alloying elements as predictors. A total of 149 cubic perovskites containing fluorides, chlorides, and bromides with cation combinations of A1+B2+, as well as oxides with cation combinations of A1+B5+, A2+B4+, and A3+B3+ are explored. The model demonstrates good performance in terms of stabilities and accuracy, and thus could be a rapid approach to estimate lattice constants. Perovskite; Crystal structure; Oxide; Halide; Machine learning |
| ArticleNumber | e07601 |
| Author | Xu, Xiaojie Zhang, Yun |
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| Keywords | Oxide Perovskite Halide Machine learning Crystal structure |
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| Snippet | Perovskites having the chemical formulae of ABX3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important... Perovskites having the chemical formulae of ABX are promising candidates for various electronic, magnetic, and thermal applications. One of the important... Perovskites having the chemical formulae of ABX₃ are promising candidates for various electronic, magnetic, and thermal applications. One of the important... Perovskites having the chemical formulae of ABX 3 are promising candidates for various electronic, magnetic, and thermal applications. One of the important... |
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| SubjectTerms | cations Crystal structure Halide Machine learning magnetism normal distribution Oxide Perovskite |
| Title | Modeling of lattice parameters of cubic perovskite oxides and halides |
| URI | https://dx.doi.org/10.1016/j.heliyon.2021.e07601 https://www.ncbi.nlm.nih.gov/pubmed/34355095 https://www.proquest.com/docview/2559425657 https://www.proquest.com/docview/2636464135 https://pubmed.ncbi.nlm.nih.gov/PMC8321928 https://doaj.org/article/9a96e33cd2fd47d9a203dcd57bfe5de0 |
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