A machine learning approach to model solute grain boundary segregation
| العنوان: | A machine learning approach to model solute grain boundary segregation |
|---|---|
| المؤلفون: | Raheleh Hadian, Jörg Neugebauer, Blazej Grabowski, Liam Huber |
| المصدر: | npj Computational Materials npj Computational Materials, Vol 4, Iss 1, Pp 1-8 (2018) |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, Lattice (order), 0103 physical sciences, Atom, lcsh:TA401-492, General Materials Science, Embrittlement, 010302 applied physics, lcsh:Computer software, Structural material, business.industry, 021001 nanoscience & nanotechnology, Surface energy, Computer Science Applications, Data point, lcsh:QA76.75-76.765, Mechanics of Materials, Modeling and Simulation, Density of states, Grain boundary, lcsh:Materials of engineering and construction. Mechanics of materials, Artificial intelligence, 0210 nano-technology, business, computer |
| الوصف: | 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. Undecorated grain boundaries can yield accurate descriptors to predict isotherms of interfacial energy changes. Liam Huber and others at the Max Planck Institute fur Eisenforschung in Dusseldorf developed a framework to compute the segregation energy distributions in aluminium. They first performed a high-throughput study of six solute species segregating at thousands of sites at thirty-eight different types of low and high-symmetry boundaries. They then realistically described the segregation density of states. Using machine learning, they finally identified descriptors which depend only on the local properties of the solute-free grain boundaries, successfully calculating segregation isotherms with significantly less computational effort. Routinely determining segregation isotherms for arbitrary grain boundaries may help us better understand detrimental grain boundary issues, such as embrittlement. |
| وصف الملف: | text/html |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ea00d312a05d87dc7713ba7d1502a5e https://hdl.handle.net/21.11116/0000-0003-A390-121.11116/0000-0005-89F5-C |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....9ea00d312a05d87dc7713ba7d1502a5e |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |