Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations
This paper presents a highly accurate and scalable Graph Neural Network potential for aluminum, developed through a sequential-refinement workflow, which enables million-atom simulations to reveal how stacking-fault energetics and diffusion critically influence solidification microstructures and mechanical properties, outperforming both classical and general-purpose machine learning potentials.