Tancret, F., Toda-Caraballo, I., Menou, E. & Diaz-del-Castillo, P. E. J. R. (2017) Designing high entropy alloys employing thermodynamics and Gaussian process statistical analysis. Mater. Des. 115 486–497.
Added by: Richard Baschera (2017-02-02 13:56:04) Last edited by: Richard Baschera (2017-02-06 15:35:33)
|Type de référence: Article
Numéro d'identification (ISBN etc.): 0264-1275
Clé BibTeX: Tancret2017
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|Catégories: ID2M, INTERNATIONAL
Mots-clés: accelerated exploration, Data mining, hea, model, multicomponent alloys, Neural network, parameter, phase-stability, prediction, principal element alloys, solid-solution, structural applications, Thermo-Calc
Créateurs: Diaz-del-Castillo, Menou, Tancret, Toda-Caraballo
Collection: Mater. Des.
Consultations : 19/753
Indice de consultation : 5%
Indice de popularité : 1.25%
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.