Machine learning interatomic potentials for aluminium: application to solidification phenomena.

In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphization requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and the liquid states. Taking into account rare nucleation events or structural relaxation under deep undercooling conditions requires much larger length scales and longer time scales than those achievable byab initiomolecular dynamics (AIMD). This problem is addressed by means of classical molecular dynamics simulations using a well established high dimensional neural network potential trained on a set of configurations generated by AIMD relevant for solidification phenomena. Our dataset contains various crystalline structures and liquid states at different pressures, including their time fluctuations in a wide range of temperatures. Applied to elemental aluminium, the resulting potential is shown to be efficient to reproduce the basic structural, dynamics and thermodynamic quantities in the liquid and undercooled states. Early stages of crystallization are further investigated on a much larger scale with one million atoms, allowing us to unravel features of the homogeneous nucleation mechanisms in the fcc phase at ambient pressure as well as in the bcc phase at high pressure with unprecedented accuracy close to theab initioone. In both cases, a single step nucleation process is observed.

Excess entropy scaling law: A potential energy landscape view.

The relationship between excess entropy and diffusion is revisited by means of large-scale computer simulation combined to supervised learning approach to determine the excess entropy for the Lennard-Jones potential. Results reveal a strong correlation with the properties of the potential energy landscape (PEL). In particular the exponential law holding in the liquid is seen to be linked with the landscape-influenced regime of the PEL whereas the fluidlike power-law corresponds to the free diffusion regime.

Numerical modelling of acoustic streaming during the ultrasonic melt treatment of direct-chill (DC) casting.

Acoustic streaming and its attendant effects in the sump of a direct-chill (DC) casting process are successfully predicted under ultrasonic treatment for the first time. The proposed numerical model couples acoustic cavitation, fluid flow, heat and species transfer, and solidification to predict the flow pattern, acoustic pressure, and temperature fields in the sump. The model is numerically stable with time steps of the order of 0.01 s and therefore computationally attractive for optimization studies necessitating simulation times of the order of a minute. The sump profile is altered by acoustic streaming, with the slurry region depressed along the centreline of the billet by a strong central jet. The temperature gradient in the transition zone is increased, potentially interfering with grain refinement. The cooling rate in the sump is also altered, thereby modifying the dendrite arm spacing of the as-cast billet. The relative position of the sonotrode affects the sump profile, with the sump depth decreased by around 5 mm when the sonotrode is moved above the graphite ring level by 100 mm. The acoustic streaming jet penetrates into the slurry zone and, as a result, the growth direction of dendritic grains in the off-centre position is altered.