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
基金supported financially by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 51621004)the National Natural Science Foundation of China (Nos. 51871092, 11772122, 51625404, 51771232+5 种基金51671217)the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body (No. 71865015)the State Key Laboratory of Powder Metallurgythe National Key Research and Development Program of China (Nos. 2016YFB0700300 and 2016YFB1100103)support of the U.S. Army Research Office Project (Nos. W911NF-13-1-0438 and W911NF-19-2-0049) with the program managers,Drs. M.P. Bakas,S.N. Mathaudhusupport from the National Science Foundation (Nos. DMR-1611180 and 1809640)with the program directors,Drs. J. Yang,J.G. Shiflet,and D. Farkas。
文摘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.
