Aluminum alloys are attractive for a number of applications due to their high specific strength, and developing new compositions is a major goal in the structural materials community. Here, we investigate the Al-Zn-Mg-Cu alloy system (7xxx series) by machine learning-based composition and process optimization. The discovered optimized alloy is compositionally lean with a high ultimate tensile strength of 952 MPa and 6.3% elongation following a cost-effective processing route. We find that the Al₈Cu₄Y phase in wrought 7xxx-T6 alloys exists in the form of a nanoscale network structure along sub-grain boundaries besides the common irregular-shaped particles. Our study demonstrates the feasibility of using machine learning to search for 7xxx alloys with good mechanical performance.

铝合金由于具有较高的比强度而在许多应用中具有吸引力，因此开发新的成分是结构材料领域的主要目标。在这里，我们通过基于机器学习的成分和工艺优化来研究Al-Zn-Mg-Cu合金系统（7xxx系列）。所发现的优化合金成分合理，遵循经济高效的加工路线，具有952 MPa的高极限抗拉强度和6.3％的伸长率。我们发现，变形的7xxx-T6合金中的Al ₈ Cu ₄ Y相除了常见的不规则形状的颗粒外，还沿着亚晶粒边界以纳米级网络结构的形式存在。我们的研究证明了使用机器学习来搜索具有良好机械性能的7xxx合金的可行性。