机器学习辅助的高通量实验加速硬质高熵合金 CoxCryTizMouWv成分设计-中国材料进展

文章信息/Info

Title:: Machine Learning Assisted High-Throughput Experiments Accelerates the Composition Design of Hard High-Entropy Alloy CoxCryTizMouWv

作者:: 王炯1; 肖斌2; 刘轶1; 2; （1. 上海大学材料基因组工程研究院，上海 200444）（2. 上海大学物理系量子与分子结构国际中心，上海 200444）

Author(s):: WANG Jiong 1; XIAO Bin 2 ; and LIU Yi1; 2; (1. Materials Genome Institute, Shanghai University, Shanghai 200444, China) (2. International Centre for Quantum and Molecular Structures, Department of Physics, Shanghai University, Shanghai 200444, China)

Keywords:: High-throughput experiment; Machine learning; High entropy alloy; Hardness

摘要:: 针对目标性能的多元合金成分设计因具有巨大的成分参数空间而极具挑战，而且传统的试错实验由于效率低能探索的合金成分有限。提出利用高通量实验结合机器学习方法加速非等摩尔比的硬质高熵合金CoxCryTizMouWv的成分设计。首先通过自主研发的全流程高通量合金制备系统制备了138个不同成分的高熵合金铸态样品。然后根据测量的维氏硬度(HV)数据，使用随机森林法和支持向量机法进行机器学习建模，并预测了五元合金体系内潜在的3876个不同成分合金的硬度。随机森林机器学习模型的预测结果在高（HV>800 MPa）、中（600

Abstract:: The composition design of multi-component alloy for the target performance is extremely challenging due to the enormous potential composition. The traditional trial-anderror experiments can only explore limited alloy compositions because of its low efficiency. In this work, the composition design of non-equimolar hard high-entropy alloy CoxCryTizMouWv was accelerated via combining the high-throughput experiment with machine learning. Firstly, 138 as-cast high-entropy alloys were prepared by a home-developed all-process high-throughput alloy synthesis system. Then, the machine learning models were built based on the measured Vickers hardness (HV) by using random forest (RF) and supporting vector machine methods. And, they made the prediction of HV values for 3876 potential alloys in the fivecomponent alloy system. The HV values predicted by RF machine learning models have the averaged errors of 2.87%, 3.30% and 6.70%, respectively in high (HV>800 MPa), medium (600