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Ebook Prediction Of Defects In Material Processing
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Journal of Manufacturing Science and Engineering Chemistry of Materials Journal of Materials Science Advanced search. Skip to main content. Subjects Computational methods Metals and alloys. Abstract We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic A—B compounds, using as an example systems with the cubic B2 crystal structure with equiatomic AB stoichiometry.
Introduction In crystalline compounds, point defects often play a central role in governing a wide variety of physical properties.
Prediction of Defects in Material Processing
Full size image. Results and discussion From the Materials Project database, 26 B2-type intermetallic compounds as listed in Supplementary Information SI Table 3 were first selected for high throughput defect property calculations. DT- based classification scheme to predict the dominant defect types in B2 compounds. Conclusions In this work, we demonstrated an approach combining the high throughput DFT calculations with ML algorithms to predict dominant defect types in inorganic compounds. Methods Defect concentrations in a grand-canonical dilute-solution formalism The intrinsic point defect properties in B2 intermetallic compounds were evaluated using the computational framework recently implemented in the python code PyDI.
References 1 Carling, K.