Predicting the optimum compositions of high-performance Cu-Zn alloys via machine learning

In the alloy materials, their mechanical properties mightly rely on the compositions and concentrations of chemical elements. Therefore, looking for the optimum elemental concentration and composition is still a critical issue to design high-performance alloy materials. Traditional alloy designing method via trial and error or domain experts' experiences is barely possible to solve the issue. Here, we propose a composition-oriented method combined machine learning to design the Cu-Zn alloys with the high strengths, high ductility, and low friction coefficient. The method of separate training for each attribute label is used to study the effects of elemental concentrations on the mechanical properties of Cu-Zn alloys. Moreover, the elemental concentrations of new Cu-Zn alloys with the good mechanical properties are predicted by machine learning. The current results reveal the vital importance of the composition-oriented design method via machine learning for the development of high-performance alloys in a broad range of elemental compositions.