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Choe, H. Teng, Z. Characterization of nanoscale nial-type precipitates in a ferritic steel by electron microscopy and atom probe tomography. Mater 63 , 61—64 Song, G. Ferritic alloys with extreme creep resistance via coherent hierarchical precipitates. Subramanian, P. The development of Nb-based advanced intermetallic alloys for structural applications.

JOM 48 , 33—38 Clemens, H. Light-weight intermetallic titanium aluminides-status of research and development.


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Acta Crystallogr. Miedema, A. On the heat of formation of solid alloys. Less-Common Met 41 , — Kong, C. Information-theoretic approach for the discovery of design rules for crystal chemistry.

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Statist 18 , 50—60 Zunger, A. Systematization of the stable crystal structure of all AB-type binary compounds: a pseudopotential orbital-radii approach. B 22 , — Clementi, E. Atomic screening constants from scf functions. R Development Core Team. Hastie, T. The Elements of Statistical Learning , 2nd edn Springer, Combinatorial screening for new materials in unconstrained composition space with machine learning.


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  4. Jiang, C. First-principles study of constitutional point defects in B2 NiAl using special quasirandom structures. Site preference of transition-metal elements in B2 NiAl: A comprehensive study. Kresse, G. Ab initio molecular dynamics for liquid metals. B 47 , — Ab initio molecular-dynamics simulation of the liquid-metal21amorphous-semiconductor transition in germanium. B 49 , — Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.

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    High-precision sampling for brillouin-zone integration in metals. B 40 , — Download references. Department of Energy under Contract No. All authors analyzed the results and revised the paper. Correspondence to Bharat Medasani. This work is licensed under a Creative Commons Attribution 4. Reprints and Permissions.

    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.

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    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.