Content Provider	Taylor & Francis Online
Author	Haynes, J. Allen Lee, Sangkeun Shyam, Amit Shin, Dongwon
Abstract	Recent progress in high-performance computing and data informatics has opened up numerous opportunities to aid the design of advanced materials. Herein, we demonstrate a computational workflow that includes rapid population of high-fidelity materials datasets via petascale computing and subsequent analyses with modern data science techniques. We use a first-principles approach based on density functional theory to derive the segregation energies of 34 microalloying elements at the coherent and semi-coherent interfaces between the aluminium matrix and the θ′-Al2Cu precipitate, which requires several hundred supercell calculations. We also perform extensive correlation analyses to identify materials descriptors that affect the segregation behaviour of solutes at the interfaces. Finally, we show an example of leveraging machine learning techniques to predict segregation energies without performing computationally expensive physics-based simulations. The approach demonstrated in the present work can be applied to any high-temperature alloy system for which key materials data can be obtained using high-performance computing.
Starting Page	828
Ending Page	838
Page Count	11
File Format	PDF HTM / HTML
ISSN	14686996 18785514
DOI	10.1080/14686996.2017.1371559
Journal	Science and Technology of Advanced Materials
Volume Number	18
Issue Number	1
Language	English
Publisher	Taylor & Francis
Publisher Date	2017-10-25
Access Restriction	Open
Subject Keyword	Supercomputing First-principles calculations Density functional theory Correlation analysis Machine learning Alloys 10 Engineering and Structural materials 401 1st principle calculations 404 Materials informatics / Genomics
Content Type	Text
Resource Type	Article
Subject	Materials Science

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