Solidification control is crucial in manufacturing technologies, as it determines the microstructure and, consequently, the performance of the final product. Investigating the mechanisms occurring during the early stages of nucleation remains experimentally challenging as it initiates on nanometer length and subpicoseconds time scales. Large scale molecular dynamics simulations using machine learning interatomic potential with quantum accuracy appears the dedicated approach to complex, atomic level, multidimensional mechanisms with local symmetry breaking. A potential trained on a high-dimensional neural network on density functional theory-based ab initio molecular dynamics (AIMD) trajectories for liquid and undercooled states for Al-Si binary alloys enables us to study the nucleation mechanisms occurring at the early stages from the liquid phase near the eutectic composition. Our results indicate that nucleation starts with Al in Al-rich conditions and with Si in Si-rich conditions. Whereas Al nuclei grow in a globular shape, Si ones grow with polygonal faceting, whose underlying mechanisms are further discussed.

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• Nucleation
• Phase transitions
• Machine learning
• Molecular dynamics