Accelerated discovery of high-strength aluminum alloys by machine learning

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. The discovery of new alloys with desirable mechanical properties is traditionally a time consuming process. Here, machine learning is applied to the discovery of aluminum alloys, revealing a compositionally-lean alloy with an ultimate tensile strength of 952 MPa and 6.3% elongation.