A property-oriented design strategy for high performance copper alloys via machine learning

TLDR

Traditional material design relies on trial‑and‑error and expert experience, making it time‑ and cost‑intensive. The study proposes a machine‑learning design system to accelerate the discovery of new materials. The system integrates machine‑learning modeling, compositional design, and property prediction. It efficiently designs high‑performance copper alloys with 600–950 MPa ultimate tensile strength and 50 % IAC conductivity, and predictions closely match measured values for literature and newly fabricated alloys, offering a new recipe for property‑oriented design.

Abstract

Abstract Traditional strategies for designing new materials with targeted property including methods such as trial and error, and experiences of domain experts, are time and cost consuming. In the present study, we propose a machine learning design system involving three features of machine learning modeling, compositional design and property prediction, which can accelerate the discovery of new materials. We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600–950 MPa and an electrical conductivity of 50.0% international annealed copper standard. There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions. Our results provide a new recipe to realize the property-oriented compositional design for high-performance complex alloys via machine learning.