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High-throughput simulation combined machine learning search for optimum elemental composition in medium entropy alloy 被引量:2
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作者 Jia Li Baobin Xie +3 位作者 Qihong Fang Bin Liu Yong Liu Peter K.Liawc 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2021年第9期70-75,共6页
In medium/high entropy alloys, their mechanical properties are strongly dependent on the chemicalelemental composition. Thus, searching for optimum elemental composition remains a critical issue to maximize the mechan... In medium/high entropy alloys, their mechanical properties are strongly dependent on the chemicalelemental composition. Thus, searching for optimum elemental composition remains a critical issue to maximize the mechanical performance. However, this issue solved by traditional optimization process via "trial and error" or experiences of domain experts is extremely difficult. Here we propose an approach based on high-throughput simulation combined machine learning to obtain medium entropy alloys with high strength and low cost. This method not only obtains a large amount of data quickly and accurately,but also helps us to determine the relationship between the composition and mechanical properties.The results reveal a vital importance of high-throughput simulation combined machine learning to find best mechanical properties in a wide range of elemental compositions for development of alloys with expected performance. 展开更多
关键词 Medium entropy alloy optimum elemental composition High-throughput simulation Machine learning
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