| 1. |
- Castin, N., et al.
(författare)
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Advanced atomistic models for radiation damage in Fe-based alloys : Contributions and future perspectives from artificial neural networks
- 2018
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Ingår i: Computational materials science. - : Elsevier. - 0927-0256 .- 1879-0801. ; 148, s. 116-130
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Castin, N., et al.
(författare)
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The dominant mechanisms for the formation of solute-rich clusters in low-Cu steels under irradiation
- 2020
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Ingår i: Materials Today Energy. - : Elsevier BV. - 2468-6069. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- The formation of nano-sized, coherent, solute-rich clusters (NSRC) is known to be an important factor causing the degradation of the macroscopic properties of steels under irradiation. The mechanisms driving their formation are still debated. This work focuses on low-Cu reactor pressure vessel (RPV) steels, where solute species are generally not expected to precipitate. We rationalize the processes that take place at the nanometer scale under irradiation, relying on the latest theoretical and experimental evidence on atomic-level diffusion and transport processes. These are compiled in a new model, based on the object kinetic Monte Carlo (OKMC) technique. We evaluate the relevance of the underlying physical assumptions by applying the model to a large variety of irradiation experiments. Our model predictions are compared with new experimental data obtained with atom probe tomography and small angle neutron scattering, complemented with information from the literature. The results of this study reveal that the role of immobilized self-interstitial atoms (SIA) loops dominates the nucleation process of NSRC.
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| 3. |
- Castin, N., et al.
(författare)
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The effect of rhenium on the diffusion of small interstitial clusters in tungsten
- 2020
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Ingår i: Computational materials science. - : ELSEVIER. - 0927-0256 .- 1879-0801. ; 177
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we use atomistic simulations to investigate the mobility and stability of self-interstitial atom (SIA) clusters of size 1-5 in W-Re alloys. We apply molecular statics and molecular dynamics (MD) simulations to determine the dimensionality of diffusion of the clusters, the activation energy of translation and rotation, and the energy of dissociation. The results show a strong effect of Re on the diffusion properties of SIA clusters, but not on its stability. The diffusion mechanism of the single SIA changes from 1-D migration with on-site rotations to full 3-D diffusion on the MD time and length scale due to the addition of Re. Further, the mobility of the SIA clusters is greatly reduced by the addition of Re. The obtained results can be readily used to parameterize coarse grain models such as object kinetic Monte Carlo and rate theory models.
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| 4. |
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| 5. |
- Messina, Luca, 1986-, et al.
(författare)
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A DFT-driven multifidelity framework for constructing efficient energy models for atomic-scale simulations
- 2020
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Ingår i: Nuclear Instruments and Methods in Physics Research Section B. - : Elsevier BV. - 0168-583X .- 1872-9584. ; 483, s. 15-21
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Tidskriftsartikel (refereegranskat)abstract
- The reliability of atomistic simulations depends on the quality of the underlying energy models providing the source of physical information, for instance for the calculation of migration barriers in atomistic Kinetic Monte Carlo simulations. Accurate (high-fidelity) methods are often available, but since they are usually computationally expensive, they must be replaced by less accurate (low-fidelity) models that introduce some degrees of approximation. Machine-learning techniques such as artificial neural networks can be employed to work around this limitation and extract the needed parameters from large databases of high-fidelity data. However, the latter are often computationally expensive to produce. This work introduces an alternative method based on the multifidelity approach. Correlations between high-fidelity and low-fidelity predictions are exploited to make an educated guess of the high-fidelity value based only on quick low-fidelity estimations, to be used for instance as an efficient and reliable source of physical data for atomistic simulations. With respect to neural networks, this approach requires less training data because of the lower amount of fitting parameters involved. The method is tested on the prediction of ab initio formation and migration energies of vacancy diffusion in iron-copper alloys, and compared with the neural networks trained on the same database.
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| 6. |
- Messina, Luca, 1986-, et al.
(författare)
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Ab initio-based investigation of solute-dumbbell transport and radiation induced segregation in Fe-X (X=Cr, Cu, Mn, Ni, P, Si) dilute alloys
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this work are analyzed the solute-transport mechanisms due to coupling with dumbbell-type defects in iron alloys, for selected impurities, by combining ab initio calculations of defect transition rates with a mean-field treatment yielding the transport coefficients of the alloy. Average radiation-induced segregation tendencies are determined based on these results and the vacancy-diffusion tendencies derived in a previous study. A new mathematical framework allows for such tendencies to be expressed in terms of vacancy-solute and dumbbell-solute flux-coupling, as well as the relative efficiency of the two mechanisms. The results show that P, Mn, and Cr to a lesser extent are transported by dumbbells thanks to the combination of high mixed-dumbbell stability and mobility, whereas Cu, Ni, and Si impurities are not. For the latter impurities the vacancy mechanism is dominant, which entails solute enrichment at low temperature and depletion above the drag transition temperature. For P and Mn, the mixed-dumbbell mechanism is dominant and leads to consistent enrichment at defect sinks, independently of temperature. Finally, the RIS tendency for Cr is the outcome of a balance between enrichment due to dumbbells and depletion due to vacancies, leading to a switchover between enrichment and depletion at 460 K. The results are in qualitative agreement with resistivity-recovery experiments and experimental RIS observations in ferritic alloys.
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| 7. |
- Messina, Luca, 1986-, et al.
(författare)
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Introducing ab initio based neural networks for transition-rate prediction in kinetic Monte Carlo simulations
- 2017
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Ingår i: Physical Review B. - 2469-9950 .- 2469-9969. ; 95:6
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Tidskriftsartikel (refereegranskat)abstract
- The quality of kinetic Monte Carlo (KMC) simulations of microstructure evolution in alloys relies on the parametrization of point-defect migration rates, which are complex functions of the local chemical composition and can be calculated accurately with ab initio methods. However, constructing reliable models that ensure the best possible transfer of physical information from ab initio to KMC is a challenging task. This work presents an innovative approach, where the transition rates are predicted by artificial neural networks trained on a database of 2000 migration barriers, obtained with density functional theory (DFT) in place of interatomic potentials. The method is tested on copper precipitation in thermally aged iron alloys, by means of a hybrid atomistic-object KMC model. For the object part of the model, the stability and mobility properties of copper-vacancy clusters are analyzed by means of independent atomistic KMC simulations, driven by the same neural networks. The cluster diffusion coefficients and mean free paths are found to increase with size, confirming the dominant role of coarsening of medium- and large-sized clusters in the precipitation kinetics. The evolution under thermal aging is in better agreement with experiments with respect to a previous interatomic-potential model, especially concerning the experiment time scales. However, the model underestimates the solubility of copper in iron due to the excessively high solution energy predicted by the chosen DFT method. Nevertheless, this work proves the capability of neural networks to transfer complex ab initio physical properties to higher-scale models, and facilitates the extension to systems with increasing chemical complexity, setting the ground for reliable microstructure evolution simulations in a wide range of alloys and applications.
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| 8. |
- Messina, Luca, 1986-, et al.
(författare)
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Introducing ab initio-based neural networks for transition-rate prediction in kinetic Monte Carlo simulations
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This work presents an innovative approach to kinetic Monte Carlo (KMC) simulations, in which atomic transition rates are predicted by an artificial neural network trained on ab initio migration barriers. The method is applied to the parameterization of a hybrid atomistic-object KMC model to simulate copper precipitation during thermal aging in iron. The stability and mobility of copper clusters containing one vacancy is analyzed by means of independent atomistic KMC simulations driven by the same neural network, with the aim of parameterizing the object KMC part of the model. Copper clusters are found to be more stable and mobile with respect to previous studies, and can cover longer diffusion paths, reaching up to a few lattice units. The mean free path increases with cluster size up to around 100 copper atoms. In addition, the emission of the vacancy often occurs concurrently with the emission of one or more copper atoms, because of strong vacancy-copper correlations and kinetic coupling. In the hybrid KMC simulations, the density of copper clusters is overestimated because of the excessively high solution energy predicted by the ab initio method. Nevertheless, this work proves the capability of neural networks to transfer detailed ab initio thermodynamic and kinetic properties to the KMC model, and sets the ground for reliable microstructure evolution simulations in a wide range of alloys.
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| 9. |
- Messina, Luca, 1986-
(författare)
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Multiscale modeling of atomic transport phenomena in ferritic steels
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Defect-driven transport of impurities plays a key role in the microstructure evolution of alloys, and has a great impact on the mechanical properties at the macroscopic scale. This phenomenon is greatly enhanced in irradiated materials because of the large amount of radiation-induced crystal defects (vacancies and interstitials). For instance, the formation of nanosized solute clusters in neutron-irradiated reactor pressure vessel (RPV) ferritic steels has been shown to hinder dislocation motion and induce hardening and embrittlement. In Swedish RPV steels, this mechanical-property degradation is enhanced by the high content of manganese and nickel impurities. It has been suggested that the formation of Mn-Ni-rich clusters (which contain also Cu, Si, and P) might be the outcome of a dynamic process, where crystal defects act both as nucleation sites and solute carriers. Solute transport by point defects is therefore a crucial mechanism to understand the origin and the dynamics of the clustering process.The first part of this work aims at modeling solute transport by point defects in dilute iron alloys, to identify the intrinsic diffusion mechanisms for a wide range of impurities. Transport and diffusion coefficients are obtained by combining accurate ab initio calculations of defect transition rates with an exact mean-field model. The results show that solute drag by single vacancies is a common phenomenon occurring at RPV temperature (about 300 °C) for all impurities found in the solute clusters, and that transport of phosphorus and manganese atoms is dominated by interstitial-type defects. These transport tendencies confirm that point defects can indeed carry impurities towards nucleated solute clusters. Moreover, the obtained flux-coupling tendencies can also explain the observed radiation-induced solute enrichment on grain boundaries and dislocations.In the second part of this work, the acquired knowledge about solute-transport mechanisms is transferred to kinetic Monte Carlo (KMC) models, with the aim of simulating the RPV microstructure evolution. Firstly, the needed parameters in terms of solute-defect cluster stability and mobility are calculated by means of dedicated KMC simulations. Secondly, an innovative approach to the prediction of transition rates in complex multicomponent alloys is introduced. This approach relies on a neural network based on ab initio-computed migration barriers. Finally, the evolution of the Swedish RPV steels is simulated in a "gray-alloy" fashion, where impurities are introduced indirectly as a modification of the defect-cluster mobilities. The latter simulations are compared to the experimental characterization of the Swedish RPV surveillance samples, and confirm the possibility that solute clusters might form on small interstitial clusters.In conclusion, this work identifies from a solid theoretical perspective the atomic-transport phenomena underlying the formation of embrittling nanofeatures in RPV steels. In addition, it prepares the ground for the development of predictive KMC tools that can simulate the microstructure evolution of a wide variety of irradiated alloys. This is of great interest not only for reactor pressure vessels, but also for many other materials in extreme environments.
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| 10. |
- Messina, Luca, 1986-, et al.
(författare)
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Solute diffusion by self-interstitial defects and radiation-induced segregation in ferritic Fe-X (X=Cr, Cu, Mn, Ni, P, Si) dilute alloys
- 2020
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Ingår i: Acta Materialia. - : Elsevier. - 1359-6454 .- 1873-2453. ; 191, s. 166-185
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Tidskriftsartikel (refereegranskat)abstract
- This work investigates solute transport due to self-interstitial defects and radiation induced segregation tendencies in dilute ferritic alloys, by computing the transport coefficients of each system based on ab initio calculations of binding energies, migration rates, as well as formation and migration vibrational entropies. The implementation of the self-consistent mean field method in the KineCluE code allows for the calculation of transport coefficients extended to arbitrary interaction ranges, crystal structures, and diffusion mechanisms. In addition, the code gives access to the diffusion and dissociation rates of small solute-defect clusters - in this case, vacancy-and dumbbell-solute pairs. The results show that the diffusivity of P, Mn, and Cr solute atoms is dominated by the dumbbell mechanism, that of Cu by vacancies, while the two mechanisms might be in competition for Ni and Si, despite the fact that the corresponding mixed dumbbells are not stable. Systematic positive radiation-induced segregation (RIS) at defect sinks is expected for P and Mn solutes due to dumbbell diffusion, and for Si due mainly to vacancy drag. Vacancy drag is also responsible for Cu and Ni enrichment at sinks below 1085 K. The RIS behavior of Cr is the outcome of a fine balance between enrichment due to the dumbbell diffusion mechanism and depletion due to the vacancy one. Therefore, for dilute Cr concentrations global enrichment occurs below 540 K, and depletion above. This threshold temperature grows with solute concentration. The findings are in qualitative agreement with experimental observations of RIS and clustering phenomena, and confirm that solute-defect kinetic coupling plays an important role in the formation of solute clusters in reactor pressure vessel steels and other alloys.
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