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In the study, the mechanism of oxidation of Ti and its alloys are clarified using both of first principles calculations and machine learning.
First, using first-principles calculations, we identified the mechanisms of the oxidation of α-Ti surfaces. In addition to the case of pure Ti case, the effect of alloying elements was also systematically analyzed. It is shown that the result of oxidation resistivity of alloys can be analyzed with their electronegativity. Next, we built a machine learning model to predict the parabolic rate constant, kp, for high temperature oxidation of Ti alloys. Exploring the experimental studies on high-temperature oxidation of Ti alloys, the dataset for machine learning was built. It is shown that the model can predict kp well.

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• In Copyright
  

キーワード: high temperature oxidation, parabolic rate constant, first principles calculations, machine learning, electronegativity

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会議: World Titanium Conference 2023(Ti-2023) (2023-06-12 - 2023-06-16)

研究助成金:

• JSPS Japan Society for the promotion of Science KAKENHI grant (numbers 21H01607 and 21H04603)

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.4873

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更新時刻: 2024-10-18 16:30:37 +0900

MDRでの公開時刻: 2024-10-18 16:30:37 +0900