Accelerated discovery of high-performance Cu-Ni-Co-Si alloys through machine learning

Cu-Ni-Co-Si alloys have been regarded as a candidate for the next-generation integrated circuits. Nevertheless, using the trial and error method to design high-performance copper alloys requires a lot of effort and time. Thus, the material design method based on machine learning is used to accelerate the exploitation of alloys. In this study, a composition-process-property database of Cu-Ni-Co-Si alloys was established, and a new strategy that could simultaneously realize the prediction of properties and the optimization of compositions and process parameters was proposed. Four groups were chosen from 38,880 candidates by the multi-performance screening method; good agreements existed between the prediction and the test. The Cu-2.3Ni-0.7Co-0.7Si alloy had the best performance among the designed alloys, and this alloy was studied in depth. The influence of the dissolution of Co in Ni2Si was analyzed from a novel perspective. Interestingly, the trace amount of Co replacing Ni to form (Ni, Co)(2)Si increased the phase dissolution temperature dramatically and shortened the coarsening rate. Affected by Co, the over-aging process was slowed down, which broadened the use range of alloys greatly. Therefore, the developed Cu-2.3Ni-0.7Co-0.7Si alloy can prove to be promising materials that meet different working conditions, and its performance was better than C70350 alloy. (C) 2021 The Authors. Published by Elsevier Ltd.

Automated summary

By utilizing machine learning for material design, a Cu-2.3Ni-0.7Co-0.7Si alloy with promising performance was developed, surpassing the properties of the C70350 alloy. This alloy has the potential to meet various working conditions and broaden the use range of alloys.