Improved atomistic Monte Carlo models based on $ab-initio$ -trained neural networks: Application to FeCu and FeCr alloys
Résumé
We significantly improve the physical models underlying atomistic Monte Carlo (MC) simulations, through
the use of $ab\ initio$ fitted high-dimensional neural network potentials (NNPs). In this way, we can incorporate
energetics derived from density functional theory (DFT) in MC, and avoid using empirical potentials that are
very challenging to design for complex alloys. We take significant steps forward from a recent work where
artificial neural networks (ANNs), exclusively trained on DFT vacancy migration energies, were used to perform
kinetic MC simulations of Cu precipitation in Fe. Here, a more extensive transfer of knowledge from DFT
to our cohesive model is achieved via the fitting of NNPs, aimed at accurately mimicking the most important
aspects of the $ab\ initio$ predictions. Rigid-lattice potentials are designed to monitor the evolution during the
simulation of the system energy, thus taking care of the thermodynamic aspects of the model. In addition,
other ANNs are designed to evaluate the activation energies associated with the MC events (migration towards
first-nearest-neighbor positions of single point defects), thereby providing an accurate kinetic modeling. Because
our methodology inherently requires the calculation of a substantial amount of reference data, we design as well
lattice-free potentials, aimed at replacing the very costly DFT method with an approximate, yet accurate and
considerably more computationally efficient, potential. The binary FeCu and FeCr alloys are taken as sample
applications considering the extensive literature covering these systems.
Origine : Publication financée par une institution