Machine learning-enabled multiscale modeling of mechanical deformation of aluminum and Al-SiC nanocomposites

MS Hasan and H Bayat and W de Jong and WW Xu, MATERIALS & DESIGN, 260, 115063 (2025).

DOI: 10.1016/j.matdes.2025.115063

A machine learning-enabled multiscale framework is developed for modeling the mechanical response of both pure metal and nanoparticle- reinforced metal matrix nanocomposites (MMNCs). Using aluminum-silicon carbide (Al-SiC) as an example MMNC, atomistic simulations reveal three distinct deformation mechanisms (i.e., defect-free, dislocation-based, and interface separation) governed by the interfaces between the Al matrix and SiC nanoparticles. As compared with single crystal Al, the lattice undergoes a more abrupt failure once the dislocation network becomes extensive and void nucleation initiates, whereas in Al-SiC, nanoparticle interfaces enable a more gradual progression of damage. These mechanisms are captured through a combined classification- regression neural network surrogate model that bridges atomic-scale insights with continuum-scale finite element analysis. Machine learning- enabled multiscale modeling of pure Al accurately predicted strain localization and confirmed by in-situ scanning electron microscopic tensile testing on perforated Al specimens. This study underscores the promise of integrating physics-informed machine learning with hierarchical modeling to capture the interface dominated phenomena and guide the design of advanced MMNCs.

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