Journal
MATERIALS & DESIGN
Volume 115, Issue -, Pages 486-497Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2016.11.049
Keywords
HEA; Neural network; Thermo-Calc; Data mining
Categories
Funding
- European Union [656249]
- Accelerated Metallurgy Project - European Commission [NMP4-LA-2011-263206]
- European Space Agency
- Marie Curie Actions (MSCA) [656249] Funding Source: Marie Curie Actions (MSCA)
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High entropy alloys (HEAs), a category of highly concentrated multicomponent alloys, have become a subject of interest in the past years due to their combination of properties. The development of these single phase solid solution alloys, containing between 5% and 35% of at least five different elements, has mainly relied on trial-and-error experiments, and more recently on modelling. The latter has notably focused on criteria to guide the formation of a single solid solution: (1) Hume-Rothery rules or their modification based on elemental variations in atomic radius, electronegativity, valence or number of itinerant electrons; (2) the use of thermodynamic concepts relying on estimates of enthalpy or entropy of mixing, and/or on melting or spinodal decomposition temperatures; (3) criteria based on lattice distortion; and (4) computational thermodynamics using the CALculation of PHAse Diagrams (CALPHAD) method. However, none of these criteria or methods, taken alone, can reliably predict the formation of a single solid solution. Instead, based on a critical assessment and a Gaussian process statistical analysis, a robust strategy to predict the formation of a single solid solution is proposed, taking into account most of the previously proposed criteria simultaneously. The method can be used as a guide to design new HEAs. (C) 2016 Elsevier Ltd. All rights reserved.
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