Models and regressions to describe primary damage in silicon carbide.

Silicon carbide (SiC) and SiC/SiC composites are important candidate materials for use in the nuclear industry. Coarse grain models are the only tools capable of modelling defect accumulation under different irradiation conditions at a realistic time and length scale. The core of any such model is the so-called "source term", which is described by the primary damage. In the present work, classical molecular dynamics (MD), binary collision approximation (BCA) and NRT model are applied to describe collision cascades in 3C-SiC with primary knock-on atom (PKA) energy in the range 1-100 keV. As such, BCA and NRT are benchmarked against MD. Particular care was taken to account for electronic stopping and the use of a threshold displacement energy consistent with density functional theory and experiment. Models and regressions are developed to characterize the primary damage in terms of number of stable Frenkel pairs and their cluster size distribution, anti-sites, and defect type. As such, an accurate cascade database is developed with simple descriptors. One of the main results shows that the defect cluster size distribution follows the geometric distribution rather than a power law.

On the mobility of vacancy clusters in reduced activation steels: an atomistic study in the Fe-Cr-W model alloy.

Reduced activation steels are considered as structural materials for future fusion reactors. Besides iron and the main alloying element chromium, these steels contain other minor alloying elements, typically tungsten, vanadium and tantalum. In this work we study the impact of chromium and tungsten, being major alloying elements of ferritic Fe-Cr-W-based steels, on the stability and mobility of vacancy defects, typically formed under irradiation in collision cascades. For this purpose, we perform ab initio calculations, develop a many-body interatomic potential (EAM formalism) for large-scale calculations, validate the potential and apply it using an atomistic kinetic Monte Carlo method to characterize the lifetime and diffusivity of vacancy clusters. To distinguish the role of Cr and W we perform atomistic kinetic Monte Carlo simulations in Fe-Cr, Fe-W and Fe-Cr-W alloys. Within the limitation of transferability of the potentials it is found that both Cr and W enhance the diffusivity of vacancy clusters, while only W strongly reduces their lifetime. The cluster lifetime reduction increases with W concentration and saturates at about 1-2 at.%. The obtained results imply that W acts as an efficient 'breaker' of small migrating vacancy clusters and therefore the short-term annealing process of cascade debris is modified by the presence of W, even in small concentrations.

Correlated recombination and annealing of point defects in dilute and concentrated Fe-Cr alloys.

In this work, we present a comprehensive combined modelling approach to study the annealing of lattice defects in dilute and concentrated metallic alloys. The developed approach consists in the combination of molecular dynamics, atomistic kinetic Monte Carlo (AKMC) and mean field rate theory methods, linked at appropriate time and space scales. For the first time, the AKMC tool has been designed to model the evolution of point defects (both vacancies and self-interstitial atoms) in random concentrated alloys, taking into account the influence of lattice distortion on the local migration energy barrier due to the mutual interaction of point defects and solutes. Good accuracy and outstanding speed of calculations has been achieved by introducing the artificial neural network regression as an engine of the AKMC applied to calculate migration barriers for mobile defects. The developed method was applied to study correlated recombination in bcc Fe and random Fe-Cr alloys, aiming at the reproduction of a set of experimental studies after electron irradiation. The obtained results agree well with the available experimental data, implying that the developed modelling procedure correctly captures the undergoing physical process.

Modeling the first stages of Cu precipitation in α-Fe using a hybrid atomistic kinetic Monte Carlo approach.

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.

Calculation of proper energy barriers for atomistic kinetic Monte Carlo simulations on rigid lattice with chemical and strain field long-range effects using artificial neural networks.

In this paper we take a few steps further in the development of an approach based on the use of an artificial neural network (ANN) to introduce long-range chemical effects and zero temperature relaxation (elastic strain) effects in a rigid lattice atomistic kinetic Monte Carlo (AKMC) model. The ANN is trained to predict the vacancy migration energies as calculated given an interatomic potential with the nudged elastic band method, as functions of the local atomic environment. The kinetics of a single-vacancy migration is thus predicted as accurately as possible, within the limits of the given interatomic potential. The detailed procedure to apply this method is described and analyzed in detail. A novel ANN training algorithm is proposed to deal with the necessarily large number of input variables to be taken into account in the mathematical regression of the migration energies. The application of the ANN-based AKMC method to the simulation of a thermal annealing experiment in Fe-20%Cr alloy is reported. The results obtained are found to be in better agreement with experiments, as compared to already published simulations, where no atomic relaxation was taken into account and chemical effects were only heuristically allowed for.

Pretransplant hepatitis C virus infection: a predictor of proteinuria after renal transplantation.

BACKGROUND: Reports have suggested that hepatitis C virus (HCV)-infected kidney recipients may develop de novo glomerular lesions caused by the virus. We studied the relationships between pretransplantation anti-HCV antibodies and the occurrence of proteinuria and the link with short- and long-term patient and graft survival. METHODS: A total of 322 consecutive renal recipients treated at a single center from 1989 to 1994 whose sera were routinely assayed for anti-HCV antibodies at the time of transplantation were analyzed. The risks of persistent proteinuria (>1 g/day), graft loss, or death were estimated by Kaplan-Meier analysis. The relationship between clinical variables and each outcome was examined by Cox multivariate regression analysis. RESULTS: Before transplantation, 9.6% of the recipients were anti-HCV antibody positive. Persistent proteinuria developed in 13.6% recipients. The presence of anti-HCV antibodies was strongly associated with proteinuria (relative risk [RR]=5.36, 95% confidence interval [CI]=2.49-11.51). Proteinuria occurred more frequently in second grafts (RR=2.64, 95% CI=1.10-6.29). The number of HLA-A,B mismatches was an independent risk factor (RR=1.55, 95% CI=1.10-2.19). Recipient age (RR=0.80, 95% CI=0.63-1.02) and duration of dialysis (RR=0.86, 95% CI=0.77-0.96) were protective factors. Histology of biopsies from 26/44 recipients with proteinuria showed that de novo glomerular lesions were more frequent in HCV-positive patients, although the difference was not significant. One- and five-year graft survival rates were significantly worse in patients with proteinuria (90.7% and 41.1%) than in patients without it (95.6% and 91.8%) (P<0.00001). Despite the strong association between HCV infection and proteinuria, patient and graft survival rates in anti-HCV-positive and anti-HCV-negative recipients were similar. CONCLUSIONS: The presence of anti-HCV antibodies before renal transplantation seems to be a major risk factor of proteinuria after transplantation. This may be due to glomerular lesions caused by HCV. However, anti-HCV has no impact on 5-year patient and graft survival.