Microstructure and properties of Cu-3Ti-0.3Cr-0.15Mg alloy designed via machine learning

With the development of information technology, machine learning (ML) has been widely applied in materials science. ML can avoid a multitude of trial and error experiments and improve the efficiency of discovering new materials. Hence, screening the alloys with excellent properties by machine learning is time-saving and effective. In this work, the major element descriptors were screened by importance analysis and correlation analysis as input features, and common ML algorithms were considered to establish ML model. The final 'composition- process-property' alloy design strategy was proposed and applied to guide the selection of alloy systems and composition. As a result, the Cu-3Ti-0.3Cr-0.15 Mg alloy was screened based on the extreme gradient boosting (XGBoost) model. The ultimate tensile strength and electrical conductivity of 1018 MPa and 20.1 % IACS were achieved for the designed alloy, respectively, which is well consistent with the predicted values. The ultra-high strength for the designed alloy can be mainly attributed to the precipitation strengthening. Specifically, the formation of nanoscale beta '-Cu4Ti '-Cu 4 Ti and Cr precipitates during the heat treatment plays a significant role in enhancing the strength of the alloy. This work offers new insights into the design of Cu-Ti alloys with excellent comprehensive properties by employing a machine learning model trained on small data sets, which also provides a potential to be applied to other materials systems.