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Machine learning, and more specifically artificial neural networks (ANN), are powerful and flexible numerical tools that can lead to significant improvements in many materials modelling techniques. This paper provides a review of the efforts made so far to describe the effects of irradiation in Fe-based and W-based alloys, in a multiscale modelling framework. ANN were successfully used as innovative parametrization tools in these models, thereby greatly enhancing their physical accuracy and capability to accomplish increasingly challenging goals. In the provided examples, the main goal of ANN is to predict how the chemical complexity of local atomic configurations, and/or specific strain fields, influence the activation energy of selected thermally-activated events. This is most often a more efficient approach with respect to previous computationally heavy methods. In a future perspective, similar schemes can be potentially used to calculate other quantities than activation energies. They can thus transfer atomic-scale properties to higher-scale simulations, providing a proper bridging across scales, and hence contributing to the achievement of accurate and reliable multiscale models. [ABSTRACT FROM AUTHOR]

We propose an object kinetic Monte Carlo (OKMC) model for describing the microstructural evolution in pure tungsten under neutron irradiation. We here focus on low doses (under 1 dpa), and we neglect transmutation in first approximation. The emphasis is mainly centred on an adequate description of neutron irradiation, the subsequent introduction of primary defects, and their thermal diffusion properties. Besides grain boundaries and the dislocation network, our model includes the contribution of carbon impurities, which are shown to have a strong influence on the onset of void swelling. Our parametric study analyses the quality of our model in detail, and confronts its predictions with experimental microstructural observations with satisfactory agreement. We highlight the importance for an accurate determination of the dissolved carbon content in the tungsten matrix, and we advocate for an accurate description of atomic collision cascades, in light of the sensitivity of our results with respect to correlated recombination. [ABSTRACT FROM AUTHOR]

We perform a combined study, coupling three computational methods, to assess the impact of neutron irradiation and temperature transients on the integrity of tungsten monoblocks in the future ITER device. These plasma-facing components will indeed be subject to unsteady heat loads and neutron bombardment, whose combination induces a degradation of the mechanical properties in a heterogeneous manner. Though both phenomena have received substantial attention in literature, their combined effects are not well known. The first tool is an in-house finite volume based solver for the heat conduction equation, which is dedicated to the evaluation of temperature profiles, during steady state and typical transient conditions, such as (mitigated) type I edge localized modes and slow power transients. The second tool is a multiscale object Kinetic Monte Carlo (OKMC) model, dedicated to the prediction of the microstructure evolution under high-energy neutron bombardment, given the local temperature as input. Finally, the last tool estimates the macroscopic properties of the tungsten material, given the microstructure as predicted by the OKMC tool. As a result of the combined study, we find that thermal transients alleviate the degradation of mechanical properties for the most part of the monoblock components, at the exception of the areas close to the cooling pipes where, on the contrary, the degradations kinetics are accelerated. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a novel kind of lattice-free atomistic kinetic Monte Carlo models. [•] It uses artificial neural networks to be very fast. [•] We demonstrate the feasibility by predicting vacancy migration energies around grain boundaries. [•] We fully detail the methodology to design the neural networks. [ABSTRACT FROM AUTHOR]

Dislocation loops finely disperse in bulk W are generally visible to the transition electron microscopy (TEM) after irradiation. In the absence of strong interactions, these loops would normally diffuse very fast until being sunk at grain boundaries or at the dislocation network. In this work, we evaluate the strength of two pining effects that can explain the reason why they are nevertheless observed by TEM in bulk. On the one hand, we evaluate with density functional theory (DFT) the strength of binding between isolated loops and dissolved chemical impurities. Employing classical equations of diffusion, we estimate the resulting effective diffusion coefficient of loops. On the other hand, we consider the effect of mutual elastic interactions (MEI) between the loops, applying linear elasticity. We perform a large set of kinetic Monte Carlo (KMC) simulations, aimed at evaluating the effective diffusion coefficient, accounting for multiple interactions. Finally, we draw a map that indicates the dominant pinning effect given the experimental conditions (loop size and loop number density). Comparing with a large database of experimental TEM evidence, we conclude that pinning by dissolved impurities is the dominant effect. [Display omitted] [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract Iron based industrial steels typically contain a large number of alloying elements, even so-called low alloyed steels. Under irradiation, these alloying elements form clusters that have a detrimental impact of the mechanical properties of the steel. The stability and formation mechanisms of such clusters are presently not fully understood. Therefore, in this work, we study the thermal stability and formation kinetics of small solute clusters in the bcc Fe matrix. We use density functional theory (DFT) to characterize the binding energy of vacancy/solute clusters containing Cr, Mn, Ni, Cu, Si and P, thereby exploring >700 different configurations. The constructed DFT data base is used to fit a cluster expansion (CE) for the vacancy-FeCrMnNiCuSiP system. In turn, the obtained CE is applied in atomistic kinetic Monte Carlo simulations to study the effect of Mn, Ni, Cr, Si and P on the precipitation formation in the FeCu alloy. We conclude that the addition of Mn and Ni delay the precipitation kinetics by an order of magnitude. The additional alloying with traces of P/Si further delays the kinetics by an additional order of magnitude. We found that Si plays an essential role in the formation of spatially mixed MnNiCuSi cluster. [ABSTRACT FROM AUTHOR]

The aim of the present study is to extend the knowledge about the formation and thermal stability of vacancy-type defects in tungsten under neutron irradiation, thereby mimicking the temperature and neutron flux expected in the ITER divertor. Neutron irradiation of single crystal tungsten, W(100), in the temperature range 600-1200 °C is performed up to 0.12 dpa. Positron annihilation spectroscopy is employed to detect the presence of open volume defects, while hardness tests are applied to relate the irradiation-induced defects with the modification of mechanical properties. Rationalization of the experimental results is enhanced by the application of a kinetic Monte Carlo simulation tool, applied to model the microstructural evolution under the neutron irradiation process. The relation between radiation microstructure and hardness is explained via a dispersed barrier model. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Abstract: Vacancy migration energies as functions of the local atomic configuration (LAC) in Fe–Cu alloys have been systematically tabulated using an appropriate interatomic potential for the alloy of interest. Subsets of these tabulations have been used to train an artificial neural network (ANN) to predict all vacancy migration energies depending on the LAC. The error in the prediction of the ANN has been evaluated by a fuzzy logic system (FLS), allowing a feedback to be introduced for further training, to improve the ANN prediction. This artificial intelligence (AI) system is used to develop a novel approach to atomistic kinetic Monte Carlo (AKMC) simulations, aimed at providing a better description of the kinetic path followed by the system through diffusion of solute atoms in the alloy via vacancy mechanism. Fe–Cu has been chosen because of the importance of Cu precipitation in Fe in connection with the embrittlement of reactor pressure vessels of existing nuclear power plants. In this paper the method is described in some detail and the first results of its application are presented and briefly discussed. [Copyright &y& Elsevier]