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- W4308932013 endingPage "118484" @default.
- W4308932013 startingPage "118484" @default.
- W4308932013 abstract "Two types of alloys, Cu-Ni-Co-Si and Cu-Cr-Zr, are considered candidate materials for next-generation integrated circuits due to their superior comprehensive performance. However, the rapid development of these two types of alloys remains difficult using conventional simulation techniques. Machine learning offers a new tool for accelerating the design and discovery of new materials with required property profiles. Herein, composition-process-property database of the six-element Cu-Cr-Ni-Co-Si-Zr alloys were established, and a novel strategy of customized performance design for different application environments was proposed. Then, four alloys with different performance characteristics were rapidly screened from 850,500 candidates using a multi-property segmented screening method, and the predicted results agreed well with the experimental results. Importantly, the developed Cu-1.0Cr-1.0Ni-2.5Co-0.8Si alloy was used as a bridge alloy to link the Cu-Ni-Co-Si and Cu-Cr-Zr alloys together, filling the gap in the mid-segment performance (220–240 HV, 45–65% IACS) of Cu-based alloys. Interestingly, the studied alloy was a dual-phase precipitation-strengthened alloy. It was found that the small spherical (Co, Ni)2Si phase was the main influence on the micro-hardness and strength, while the large rod-shaped Cr3Co5Si2 phase was the main reinforcing phase that affected ductility and electrical conductivity. The design method proposed in this paper accelerates the development of the Cu-Cr-Ni-Co-Si alloy system, which has great potential for application in integrated circuits and heat sinks." @default.
- W4308932013 created "2022-11-19" @default.
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- W4308932013 date "2023-01-01" @default.
- W4308932013 modified "2023-10-18" @default.
- W4308932013 title "Customized development of promising Cu-Cr-Ni-Co-Si alloys enabled by integrated machine learning and characterization" @default.
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- W4308932013 doi "https://doi.org/10.1016/j.actamat.2022.118484" @default.
- W4308932013 hasPublicationYear "2023" @default.
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