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In studying solidification process by simulations on the atomic scale, themodeling of crystal nucleation or amorphisation requires the construction ofinteratomic interactions that are able to reproduce the properties of both thesolid and the liquid states. Taking into account rare nucleation events orstructural relaxation under deep undercooling conditions requires much largerlength scales and longer time scales than those achievable by \textit{abinitio} molecular dynamics (AIMD). This problem is addressed by means ofclassical MD simulations using a well established high dimensional neuralnetwork potential trained on a relevant set of configurations generated byAIMD. Our dataset contains various crystalline structures and liquid states atdifferent pressures, including their time fluctuations in a wide range oftemperatures considering only their energy labels. Applied to elementalaluminium, the resulting potential is shown to be efficient to reproduce thebasic structural, dynamics and thermodynamic quantities in the liquid andundercooled states without the need to include neither explicitly the forcesnor all kind of configurations in the training procedure. The early stage ofcrystallization is further investigated on a much larger scale with one millionatoms, allowing us to unravel features of the homogeneous nucleation mechanismsin the fcc phase at ambient pressure as well as in the bcc phase at highpressure with unprecedented accuracy close to the \textit{ab initio} one. Inboth case, a single step nucleation process is observed.

Interatomic machine learning potentials for aluminium: application to solidification phenomena