On-the-Fly Machine Learning Force Field Study of Liquid-Al/Solid-TiB Interfaces.

Using the on-the-fly machine learning force field, simulations were performed to study the atomic structure evolution of the liquid-Al/solid-TiB interface with two different terminations, aiming to deepen the understanding of the mechanism of TiB as nucleating particles in an aluminum alloy. We conducted simulations using MLFF for up to 100 ps, enabling us to observe the interfacial properties from a deeper and more comprehensive perspective. The nucleation potential of TiB particles is determined by the formation of various ordered structures at the interface, which is significantly influenced by the termination of the TiB (0001) surface. The evolution of the interface during heterogeneous nucleation processes with different terminations is described using structural information and dynamic characteristics. The Ti-terminated surface is more prone to forming quasi-solid regions compared to the B-termination. Analysis of mean square displacement and vibrational density of states indicates that the liquid layer at the Ti-terminated interface is closer in characteristics to a solid compared to the B-terminated interface. We also found that on the TiB (0001) surface different terminations give rise to distinct ordered structures at the interfaces, which is ascribed to their different diffusion abilities.