Interatomic potentials based on neural-network machine learning (ML) approach to address the long-standing challenge of accuracy versus efficiency in molecular-dynamics simulations have recently attracted a great deal of interest. Here, utilizing Pd-Si system as a prototype, we extend the development of neural-network ML potentials to compounds exhibiting various types of bonding characteristics. The ML potential is trained by fitting to the energies and forces of both liquid and crystal structures first-principles calculations based on density-functional theory (DFT). We show that the generated ML potential captures the structural features and motifs in $\mathrm{P}{\mathrm{d}}_{82}\mathrm{S}{\mathrm{i}}_{18}$ and $\mathrm{P}{\mathrm{d}}_{75}\mathrm{S}{\mathrm{i}}_{25}$ liquids more accurately than the existing interatomic potential based on embedded-atom method (EAM). The ML potential also describes the solid-liquid interface of these systems very well. Moreover, while the existing EAM potential fails to describe the relative energies of various crystalline structures and predict wrong ground-state structures at $\mathrm{P}{\mathrm{d}}_3\mathrm{Si}$ and $\mathrm{P}{\mathrm{d}}_9\mathrm{S}{\mathrm{i}}_2$ composition, the developed ML potential predicts correctly the ground-state structures from genetic algorithm search. The efficient ML potential with DFT accuracy from our study will provide a promising scheme for accurate atomistic simulations of structures and dynamics of complex Pd-Si system.

Although the anisotropy of the solid-liquid interfacial free energy for most alloy systems is very small, it plays a crucial role in the growth rate, morphology and crystallographic growth direction of dendrites. Previous work posited a dendrite orientation transition via compositional additions. In this work we examine experimentally the change in dendrite growth behaviour in the Al-Sm (Samarium) system as a function of solute concentration and study its interfacial properties using molecular dynamics simulations. We observe a dendrite growth direction which changes from $$\langle 100\rangle$$⟨ 100 ⟩ to $$\langle 110\rangle$$⟨ 110 ⟩ as Sm content increases. The observed change in dendrite orientation is consistent with the simulation results for the variation of the interfacial free energy anisotropy and thus provides definitive confirmation of a conjecture in previous works. In addition, our results provide physical insight into the atomic structural origin of the concentration dependent anisotropy, and deepen our fundamental understanding of solid-liquid interfaces in binary alloys.

We investigate the effect of ultrasound on the evaporation and crystallization of sessile NaCl solution droplets which were positioned in traveling or standing wave ultrasound field. The experimental results indicated that the ultrasound field can significantly accelerate the evaporation rate of the sessile droplets and refine the crystal grains. By adjusting the distance between the sessile droplets and the ultrasound emitter, it is found that, in traveling wave ultrasound field, the sessile droplet evaporation time and the time for the appearance of NaCl grains exhibited a fluctuating increase as the droplet-emitter distance increased. While in the standing wave ultrasound, the sessile droplet evaporation rate increases with the increasing droplet-emitter distance. Overall, the traveling wave ultrasound field has a stronger effect on grain refinement of the sessile droplets than the standing wave ultrasound field. The grain refinement is attributed to the decrease of critical nucleation radius caused by ultrasound energy and the increase of the nucleation rate caused by the accelerated evaporation rate. In addition, the breakage of grains caused by ultrasonic cavitation would also lead to grain refinement.