In medium/high entropy alloys,their mechanical properties are strongly dependent on the chemical-elemental 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.