Post-doctorat - Machine learning based MD for two temperature metals - H/F

Description de l'offre

Since a decade Machine Learning Interatomic Potential (MLIP) has proven unprecedented capacities in reproducing DFT reference data for very diverse configurations These potentials rely on defining atomic descriptor that encode the local geometrical environment and then learn the relation between the descriptor and the reference DFT data (energy or atomic forces). This function could be as simple as a linear function or as complex as a multilayer perceptron. As the interation potential between atoms depend on their 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 t...