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A machine learning approach to model solute grain boundary segregation

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Huber,  Liam
Adaptive Structural Materials (Simulation), Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Hadian,  Raheleh
Adaptive Structural Materials (Simulation), Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Grabowski,  Blazej
Adaptive Structural Materials (Simulation), Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Neugebauer,  Jörg
Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Citation

Huber, L., Hadian, R., Grabowski, B., & Neugebauer, J. (2018). A machine learning approach to model solute grain boundary segregation. npj Computational Materials, 4(1): 64. doi:10.1038/s41524-018-0122-7.


Cite as: http://hdl.handle.net/21.11116/0000-0003-A390-1
Abstract
Even minute amounts of one solute atom per one million bulk atoms may give rise to qualitative changes in the mechanical response and fracture resistance of modern structural materials. These changes are commonly related to enrichment by several orders of magnitude of the solutes at structural defects in the host lattice. The underlying concept—segregation—is thus fundamental in materials science. To include it in modern strategies of materials design, accurate and realistic computational modelling tools are necessary. However, the enormous number of defect configurations as well as sites solutes can occupy requires models which rely on severe approximations. In the present study we combine a high-throughput study containing more than 1 million data points with machine learning to derive a computationally highly efficient framework which opens the opportunity to model this important mechanism on a routine basis. © 2018, The Author(s).