Harmonizing mechanical and thermal properties in Al/SiC superlattices: Ab initio machine-learning-potential study

CJ Yu and JH Jang and KC Ri and SM Kim and CH Pak and RJ Kim, PHYSICAL REVIEW B, 109, 075426 (2024).

DOI: 10.1103/PhysRevB.109.075426

The aluminum matrix composites (AMCs) reinforced with silicon carbide have attracted significant interest in several high-tech industries. In this work, we provide an insightful atomistic understanding of harmonizing mechanical and thermal properties of Al/SiC composites using superlattice models and ab initio machinelearning-potential calculations. Our calculations reveal the optimal value of SiC percentage as 25% similar to 27% in superlattice-type composites for combining high mechanical strength with ductility. With increasing the SiC percentage, the coefficients of thermal expansion decrease in the range of 0.5-2.5 x 10-5 K-1, while the lattice thermal conductivity increases in the range of 24-38 W m-1 K-1 at 300 K. These data will help tailor AMCs to the terms desired with the proposed ab initio based computational procedure.

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