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.

在合金材料中，它们的机械性能可能取决于化学元素的组成和浓度。因此，寻找最佳元素浓度和组成仍然是设计高性能合金材料的关键问题。传统的合金设计方法通过“试错”或领域专家的经验几乎无法解决该问题。在这里，我们提出了一种结合机器学习的“面向成分”的方法来设计具有高强度，高延展性和低摩擦系数的Cu-Zn合金。每个属性标签的单独训练方法用于研究元素浓度对Cu-Zn合金力学性能的影响。此外，通过机器学习可以预测具有良好机械性能的新型Cu-Zn合金的元素浓度。目前的结果表明，通过机器学习来“以成分为导向”的设计方法对于开发多种元素组成的高性能合金至关重要。