Homogeneous Nucleation of Undercooled Al-Ni melts via a Machine-Learned Interaction Potential

TL;DR

This paper develops a machine-learned neural network potential for Al-Ni alloys to accurately simulate homogeneous nucleation, revealing new insights into nucleation pathways and contrasting results with classical potentials.

Contribution

It introduces a high-dimensional neural network potential for Al-Ni alloys, enabling large-scale simulations of nucleation processes with improved accuracy over traditional methods.

Findings

Pure Ni nucleates in a single-step into an fcc phase

Nucleation pathway for AlNi proceeds in a single step to B2 structure

Machine learning potential validated against experimental data

Abstract

Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately describing the interactions between atoms, hich is further complicated by chemical order through cross-species interactions. The large scales needed to observe rare nucleation events are far beyond the capabilities of ab initio simulations. Machine-learning is used for overcoming these limitations in terms of both accuracy and speed, by building a high-dimensional neural network potential for binary Al-Ni alloys, which serve as a model system relevant to many industrial applications. The potential is validated against experimental diffusion, viscosity, and scattering data, and is applied to large-scale molecular dynamics simulations of homogeneous nucleation at equiatomic composition, as well as for pure Ni. Pure Ni nucleates in a single-step into an fcc crystal phase, in contrast to previous results obtained with a classical empirical potential. This highlights the sensitivity of nucleation pathways to the underlying atomic interactions. Our findings suggest that the nucleation pathway for AlNi proceeds in a single step toward a B2 structure, which is discussed in relation to the pure elements counterparts.