Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often difficult to determine experimentally. Molecular dynamics can bridge this gap, but only if the interatomic model is both accurate and computationally efficient. Here, we develop a Machine Learning Potential (MLP) for aluminum and demonstrate its near ab initio fidelity when trained with the sequential-refinement workflow that fine-tunes the model on low-energy structures. The favorable scaling of the model enables nanosecond simulations involving millions of atoms, thereby overcoming finite-size effects in simulations of polycrystalline solidification and subsequent mechanical testing. Comparison with classical potentials and recent MLP models, including a general-purpose model, shows that inaccuracies in stacking fault energetics and diffusion can lead to qualitatively incorrect solidified grain structures and post-solidification mechanical behavior. Since our framework is based on an equivariant graph neural network, it allows for straightforward extensions to multi-component systems, providing valuable guidance for the future design and fine-tuning of both specialized and universal MLPs in computational mechanics simulations.
Störmer et al. (Fri,) studied this question.