高熵合金
主成分分析
计算机科学
鉴定(生物学)
熵(时间箭头)
校长(计算机安全)
合金
人工智能
材料科学
热力学
物理
生物
冶金
植物
操作系统
作者
J. M. Rickman,Helen M. Chan,M. P. Harmer,Joshua A. Smeltzer,Christopher J. Marvel,Abhinaba Roy,Ganesh Balasubramanian
标识
DOI:10.1038/s41467-019-10533-1
摘要
Abstract The field of multi-principal element or (single-phase) high-entropy (HE) alloys has recently seen exponential growth as these systems represent a paradigm shift in alloy development, in some cases exhibiting unexpected structures and superior mechanical properties. However, the identification of promising HE alloys presents a daunting challenge given the associated vastness of the chemistry/composition space. We describe here a supervised learning strategy for the efficient screening of HE alloys that combines two complementary tools, namely: (1) a multiple regression analysis and its generalization, a canonical-correlation analysis (CCA) and (2) a genetic algorithm (GA) with a CCA-inspired fitness function. These tools permit the identification of promising multi-principal element alloys. We implement this procedure using a database for which mechanical property information exists and highlight new alloys having high hardnesses. Our methodology is validated by comparing predicted hardnesses with alloys fabricated by arc-melting, identifying alloys having very high measured hardnesses.
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