Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Advanced atomistic models for radiation damage in Fe-based alloys: Contributions and future perspectives from artificial neural networks

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
Type de document :
Article dans une revue
Liste complète des métadonnées

https://hal-edf.archives-ouvertes.fr/hal-01828010
Contributeur : Christophe Domain <>
Soumis le : lundi 2 juillet 2018 - 22:37:41
Dernière modification le : mercredi 14 avril 2021 - 03:08:43

Lien texte intégral

Identifiants

Collections

EDF

Citation

N. Castin, M.I. Pascuet, L. Messina, C. Domain, P. Olsson, et al.. Advanced atomistic models for radiation damage in Fe-based alloys: Contributions and future perspectives from artificial neural networks. Computational Materials Science, Elsevier, 2018, 148, pp.116 - 130. ⟨10.1016/j.commatsci.2018.02.025⟩. ⟨hal-01828010⟩

Partager

Métriques

Consultations de la notice

56