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Author

Ran Li

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Aug 2026

Efficient screening of low liquidus Ti-based amorphous brazing filler metals via machine learning with limited experimental iteration

This work introduces a machine learning (ML) driven iterative framework for the accelerated design of low-liquidus-temperature Ti-based amorphous alloys in multicomponent alloy systems and identifies the optimal amorphous composition Ti60Cu11Ni19Zr10, exhibiting a low liquidus temperature of 1121 K.

Xu-He Gong, Kai-Yang Deng, Jiazi Bi et al. · 1 citation

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