The degradation of austenitic stainless steels in a radiation environment is a known problem for the in-core components of nuclear light water reactors. For a better understanding of the prevailing mechanisms responsible for the materials' degradation, large-scale atomistic simulations are desirable. In this framework and as a follow-up on Bonny et al (2011 Modelling Simul. Mater. Sci. Eng. 19 085008), we developed an embedded atom method type interatomic potential for the ternary FeNiCr system to model the production and evolution of radiation defects. Special attention has been drawn to the Fe10Ni20Cr alloy, whose properties were ensured to be close to those of 316L austenitic stainless steels. The potential is extensively benchmarked against density functional theory calculations and the potential developed in our earlier work. As a first validation, the potential is used in AKMC simulations to simulate thermal annealing experiments in order to determine the self-diffusion coefficients of the components in FeNiCr alloys around the Fe10Ni20Cr composition. The results from these simulations are consistent with experiments, i.e., DCr > DNi > DFe.
In recent years the development of atomistic models dealing with microstructure evolution and subsequent mechanical property change in reactor pressure vessel steels has been recognised as an important complement to experiments. In this framework, a literature study has shown the necessity of many-body interatomic potentials for multi-component alloys. In this paper we develop a ternary many-body Fe-Cu-Ni potential for this purpose. As a first validation, we used it to perform a simulated thermal annealing study of the Fe-Cu and FeCu-Ni alloys. Good qualitative agreement with experiments is found, although fully quantitative comparison proved impossible, due to limitations in the used simulation techniques. These limitations are also briefly discussed here.
We simulate the coherent stage of Cu precipitation in α-Fe with an atomistic kinetic Monte Carlo (AKMC) model. The vacancy migration energy as a function of the local chemical environment is provided on-the-fly by a neural network, trained with high precision on values calculated with the nudged elastic band method, using a suitable interatomic potential. To speed up the simulation, however, we modify the standard AKMC algorithm by treating large Cu clusters as objects, similarly to object kinetic Monte Carlo approaches. Seamless matching between the fully atomistic and the coarse-grained approach is achieved again by using a neural network, that provides all stability and mobility parameters for large Cu clusters, after training on atomistically informed results. The resulting hybrid algorithm allows long thermal annealing experiments to be simulated, within a reasonable CPU time. The results obtained are in very good agreement with several series of experimental data available from the literature, spanning over different conditions of temperature and alloy composition. We deduce from these results and relevant parametric studies that the mobility of Cu clusters containing one vacancy plays a central role in the precipitation mechanism.
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