Suppr超能文献

强多组分金属固溶体的高通量发现与设计

High Throughput Discovery and Design of Strong Multicomponent Metallic Solid Solutions.

作者信息

Coury Francisco G, Clarke Kester D, Kiminami Claudio S, Kaufman Michael J, Clarke Amy J

机构信息

Center for Advanced Non-Ferrous Structural Alloys, George S. Ansell Department of Metallurgical and Materials Engineering, Colorado School of Mines, Golden, CO, 80401, USA.

Departamento de Engenharia de Materiais, Universidade Federal de São Carlos, Rodovia Washington Luís, km 235, São Carlos, SP, 13565-905, Brazil.

出版信息

Sci Rep. 2018 Jun 5;8(1):8600. doi: 10.1038/s41598-018-26830-6.

Abstract

High Entropy Alloys (HEAs) are new classes of structural metallic materials that show remarkable property combinations. Yet, often times interesting compositions are still found by trial and error. Here we show an "Effective Atomic Radii for Strength" (EARS) methodology, together with different semi-empirical and first-principle models, can be used to predict the extent of solid solution strengthening to discover and design new HEAs with unprecedented properties. We have designed a CrNiCo alloy with a yield strength over 50% greater with equivalent ductility than the strongest HEA (CrNiCo) from the CrMnFeNiCo family reported to date. We show that values determined by the EARS methodology are more physically representative of multicomponent concentrated solid solutions. Our methodology permits high throughput, property-driven discovery and design of HEAs, enabling the development of future high-performance advanced materials for extreme environments.

摘要

高熵合金(HEAs)是一类新型的结构金属材料,展现出卓越的性能组合。然而,有趣的成分往往仍需通过反复试验来发现。在此我们表明,“强度有效原子半径”(EARS)方法,连同不同的半经验模型和第一性原理模型,可用于预测固溶强化程度,以发现和设计具有前所未有的性能的新型高熵合金。我们设计了一种CrNiCo合金,其屈服强度比迄今报道的CrMnFeNiCo系中最强的高熵合金(CrNiCo)高出50%以上,且具有相当的延展性。我们表明,通过EARS方法确定的值在物理上更能代表多组分浓固溶体。我们的方法允许高通量、性能驱动的高熵合金发现与设计,从而推动未来用于极端环境的高性能先进材料的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cde1/5988724/3e8f14475b15/41598_2018_26830_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验