Modelling radiation-induced phase changes in binary FeCu and ternary FeCuNi alloys using an artificial intelligence-based atomistic kinetic Monte Carlo approach

We apply a novel AKMC model, which includes local chemistry and relaxation effects when assessing the migration energy barriers of point defects, to study microchemical evolution driven by vacancy diffusion in FeCu and FeCuNi alloys. These are of importance for nuclear applications because Cu precipitation, enhanced by the presence of Ni, causes hardening and embrittlement in reactor pressure vessel steels used in existing nuclear power plants. Local chemistry and relaxation effects are introduced using artificial intelligence techniques, namely a conveniently trained artificial neural network, to calculate migration energy barriers of vacancies as functions of the local atomic configuration. We prove that the use of the neural network is equivalent to calculating the migration energy barriers on-the-fly, using computationally expensive methods such as nudged-elastic-bands with an interatomic potential. The use of the neural network makes the computational cost affordable, so that simulations of the same type as those hitherto carried out using heuristic formulas for the assessment of the energy barriers can now be performed, at the same computational cost, using more rigorously calculated barriers. This method opens the way to properly treating more complex problems, such as the case of self-interstitial cluster formation, in an AKMC framework.