Designing high entropy alloys employing thermodynamics and Gaussian process statistical analysis

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• 作者：Franck Tancret^a ; ^b ; ^{franck.tancret@univ-nantes.fr} ; Isaac Toda-Caraballo^a ; Edern Menou^b ; Pedro Eduardo Jose Rivera Dí ; az-Del-Castillo^a
• 关键词：HEA ; Neural network ; Thermo-Calc ; Data mining
• 刊名：Materials & Design
• 出版年：2017
• 出版时间：5 February 2017
• 年：2017
• 卷：115
• 期：Complete
• 页码：486-497
• 全文大小：1013 K
• 卷排序：115

文摘

CALPHAD performance is assessed on > 320 highly concentrated multicomponent alloys using several thermodynamic databases. CALPHAD or couples of physical parameters alone cannot predict reliably the formation of High Entropy Alloys (HEAs). Gaussian processes give a probabilistic prediction of single phase formation in highly concentrated multicomponent alloys. Combining CALPHAD and Gaussian processes allows to define a robust criterion for HEA formation. The new approach can be used as a tool to design new HEAs.