Machine-learning-based intelligent framework for discovering refractory high-entropy alloys with improved high-temperature yield strength  

作　　者：Stephen A.Giles Debasis Sengupta Scott R.Broderick Krishna Rajan 

机构地区：[1]CFD Research Corporation,6820 Moquin Dr.NW,Huntsville,AL,35806,US [2]Department of Material Design and Innovation,University at Buffalo,120 Bonner Hall,Buffalo,NY,14260,USA

出　　处：《npj Computational Materials》2022年第1期2247-2257,共11页计算材料学（英文）

基　　金：This work was funded by the Office of Naval Research of the United States under the Small Business Technology Transfer program(contract#N68335-20-C-0402).

摘　　要：Refractory high-entropy alloys(RHEAs)show significant elevated-temperature yield strengths and have potential to use as high-performance materials in gas turbine engines.Exploring the vast RHEA compositional space experimentally is challenging,and a small fraction of this space has been explored to date.This work demonstrates the development of a state-of-the-art machine learning framework coupled with optimization methods to intelligently explore the vast compositional space and drive the search in a direction that improves high-temperature yield strengths.Our yield strength model is shown to have a significantly improved predictive accuracy relative to the state-of-the-art approach,and also provides inherent uncertainty quantification through the use of repeated k-fold cross-validation.Upon developing and validating a robust yield strength prediction model,the coupled framework is used to discover RHEAs with superior high temperature yield strength.We have shown that RHEA compositions can be customized to have maximum yield strength at a specific temperature.

关 键 词：STRENGTH ALLOYS ENTROPY 

分 类 号：TG139[一般工业技术—材料科学与工程] TP181[金属学及工艺—合金]