The advent of femtosecond lasers has shed new light on non-equilibrium physics. The rapid energy
absorption by electrons and their subsequent energy transfer to the lattice results in non-equilibrium states of matter, initiating a new class of non-thermal processes from ambient solids to extreme conditions of temperature and pressure. The dynamic interplay between electrons and the atomic structure is the centralissue driving ultrafast phase transitions. However, the time scale of phase transitions and the microscopic mechanism driving melting are still not well understood. Classical molecular dynamics is well-suited to address this question, but classical potentials are limited in their ability to describe phenomena induced by electronic structure. DFT-based molecular dynamics could overcome this limitation, but it cannot reach the number of atoms necessary to provide a realistic picture. Machine learning potentials fitted on DFTsimulations can bridge this gap. As the interation potential between atoms depend ontheir electronic temperature we propose to learn and incorporate this dependance directly into the MLIP. Then, the Two-Temperature Model, where the diffusion equation for the electronic temperature is solved on a grid and the ionic motion is solved using MD will be employed to investigate out-of-equilibrium effects on the melting dynamics. In particular large scale MD will be used to simulate the melting of a full gold target(few tens of nm length) under laser absorption.