Crystal Nucleation in Eutectic Al-Si Alloys by Machine-Learned Molecular Dynamics

TL;DR

This study employs machine learning-enhanced molecular dynamics simulations to investigate the atomic-level nucleation mechanisms in eutectic Al-Si alloys during solidification, revealing distinct nucleation behaviors for Al and Si.

Contribution

It introduces a high-accuracy machine learning interatomic potential trained on ab initio data to simulate early-stage nucleation in Al-Si alloys, a challenging experimental process.

Findings

Al nucleates in hypoeutectic conditions

Si nucleates in hypereutectic conditions

Different growth shapes: globular for Al, polygonal faceting for Si

Abstract

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 sub-picoseconds 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 hypoeutectic conditions and with Si in hypereutectic conditions. Whereas Al nuclei grow in a globular shape, Si ones grow with polygonal faceting, whose underlying mechanisms are further discussed.