首页|Toward ultra-high strength high entropy alloys via feature engineering

Toward ultra-high strength high entropy alloys via feature engineering

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Machine learning assisted design of materials is so far based on features selected by considering the ac-curacy of model predictions,and those features do not necessarily ensure a high efficiency in searching for new materials.Here we estimate the efficiency of active learning loop by resampling method using available data as an alternative criterion for selection.The selected features allow an optimization of tar-geted property with as few new experiments as possible.Input those features into machine learning,we synthesized new high entropy alloys(HEAs)with strengths 2.8-3.0 GPa within five experimental itera-tions.The alloy AIVCrCoNiMo is found to possess compressive specific yield strengths of 397,144 and 105(MPa cm3)/g at 25,800 and 900 ℃,respectively.The specific yield strength of AIVCrCoNiMo alloy at 800 ℃ is about twice that of the commercial Inconel 718 and the typical refractory HEA of VNbMoTaW.A unique microstructure consisting of multi-scale hierarchical B2 precipitates with coherent interfaces to the BCC matrix strengthens the alloy.Our strategy of maximizing active learning efficiency provides a recipe for selecting features that accelerate the optimization of targeted property.

Machine learningFeature selectionHigh entropy alloysActive learningPrecipitation

Yan Zhang、Cheng Wen、Pengfei Dang、Turab Lookman、Dezhen Xue、Yanjing Su

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Beijing Advanced Innovation Center for Materials Genome Engineering,University of Science and Technology Beijing,Beijing,100083,China

Corrosion and Protection Center,University of Science and Technology Beijing,Beijing,100083,China

Northwest Institute for Non-ferrous Metal Research,Xi'an,710016,China

College of Mechanical Engineering,Guangdong Ocean University,Zhanjiang,524000,China

State Key Laboratory for Mechanical Behavior of Materials,Xi'an Jiaotong University,Xi'an,710049,China

AiMaterials Research LLC,Santa Fe,New Mexico,87501,United States

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2024

10.1016/j.jmst.2024.02.058

材料科学技术(英文版)
中国金属学会 中国材料研究学会 中国科学院金属研究所

材料科学技术(英文版)

CSTPCD
影响因子：0.657
ISSN：1005-0302
年,卷(期)：2024.200(33)