Importance of raw material features for the prediction of flux growth of Al<sub>2</sub>O<sub>3</sub> crystals using machine learning

Yamada, Tetsuya; Watanabe, Takanori; Hatsusaka, Kazuaki; Yuan, Jianjun; Koyama, Michihisa; Teshima, Katsuya

doi:10.1039/d2ce00010e

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Similarly, the RF algorithm was selected because it is a nonlinear regression algorithm that can provide important factors with higher accuracies than the linear regression LASSO algorithm. The regression and validation algorithms used in our previous study were also used, 23 and data cleansing was performed as described in Section 2.3.2. The LASSO λ (a penalty term) hyperparameter was optimized in the LASSO algorithm.…”

Section: Data-driven Crystal Growthmentioning

confidence: 99%

Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba₅Nb₄O₁₅ Crystals

Yamada,

Kaneko,

Hayashi

et al. 2023

Crystal Growth & Design

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Composition selection and crystal-growth control are the most important issues in material development. Crystal growth using a molten salt as the solvent, which is otherwise known as the flux method, is expected to be one of the key technologies for developing crystalline materials, because this method can grow single crystals of inorganic materials with control of the crystal habit and size. In the flux method, the number of combinations of experimental conditions usually exceeds 10,000 owing to the wide variety of experimental variables, such as solvents and heating conditions. Due to such large combinations, the conventional trial-and-error approach based on hypothesis testing would take several years to obtain the optimal crystal, and the crystal control region is also limited. As a result, the development of crystalline materials is risky, and their use is limited. To overcome this issue, we propose a novel crystal growth approach called flux-method process informatics (FPI). The FPI approach generates a data-driven experimental proposal based on machine learning predictions. More specifically, regression analyses are carried out using the experimental process as the explanatory variable and the crystal shape/size as the objective variable. In this study, FPI was applied to the layered perovskite-type oxide Ba5Nb4O15. It was found that the experimental efficiency of the FPI approach was five times higher than that of humans, and it was able to quantitatively control the shape of low-aspect-ratio crystals, which is usually difficult for anisotropic structures. These achievements provide new insights into the rapid development of high-performance crystalline materials.

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Section: Data-driven Crystal Growthmentioning

confidence: 99%

Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba₅Nb₄O₁₅ Crystals

Yamada,

Kaneko,

Hayashi

et al. 2023

Crystal Growth & Design

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“…[15,16] In the flux method, oxide crystals are generally grown at temperatures ≈1000 °C or slightly higher. [17][18][19] Crystal growth below 1000 °C facilitates the development of new single crystals. However, even the flux growth method presents a few limitations for crystal growth below 1000 °C.…”

Section: Introductionmentioning

confidence: 99%

Low‐temperature Ruby Crystal Growth Via a Supersaturation Process Based on Flux Decomposition

Ayuzawa,

Yamada,

Miyagawa

et al. 2024

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Crystal growth methods that do not require high temperatures are highly needed for the facile growth of oxide single crystals with melting points of several thousand degrees Celsius. This paper represents the first report of a method for the low‐temperature growth of ruby crystals (chromium‐doped Al2O3) at 750 °C, which is one‐third of the conventionally required temperature (2050 °C). In solution‐based crystal growth, the target crystal is grown at a temperature considerably lower than its melting point. However, conventional crystal growth processes involving solvent evaporation and cooling require high temperatures to completely liquefy the material, with previously reported solution growth temperatures of ≈1100 °C. Supersaturation based on the decomposition of crystal–solvent intermediates eliminates the need to completely liquefy the material, enabling low‐temperature crystal growth. The combination of computational and experimental investigations helps determine the optimum conditions for low‐temperature crystal growth. The proposed method is a novel green process that breaks the conventional frontiers of crystal growth while ensuring eco‐friendliness and low energy consumption. In addition, its scope can potentially be expanded to the synthesis of various crystals and direct growth on substrates with low melting points.

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Importance of raw material features for the prediction of flux growth of Al₂O₃ crystals using machine learning

Abstract: The flux method is an efficient liquid-phase crystal growth technique. It is expected to be one of the key technologies for the development of innovative inorganic materials in the future...

Cited by 2 publications

References 29 publications

Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba₅Nb₄O₁₅ Crystals

Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba₅Nb₄O₁₅ Crystals

Low‐temperature Ruby Crystal Growth Via a Supersaturation Process Based on Flux Decomposition

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Importance of raw material features for the prediction of flux growth of Al2O3 crystals using machine learning

Abstract: The flux method is an efficient liquid-phase crystal growth technique. It is expected to be one of the key technologies for the development of innovative inorganic materials in the future...

Cited by 2 publications

References 29 publications

Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba5Nb4O15 Crystals

Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba5Nb4O15 Crystals

Low‐temperature Ruby Crystal Growth Via a Supersaturation Process Based on Flux Decomposition

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Importance of raw material features for the prediction of flux growth of Al₂O₃ crystals using machine learning

Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba₅Nb₄O₁₅ Crystals

Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba₅Nb₄O₁₅ Crystals