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Artículo

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

Castin, NicolasIcon ; Pascuet, Maria Ines MagdalenaIcon ; Messina, L.; Domain, C.; Olsson, P.; Pasianot, Roberto CesarIcon ; Malerba, L.
Fecha de publicación: 06/2018
Editorial: Elsevier
Revista: Computational Materials Science
ISSN: 0927-0256
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Otras Ingenierías y Tecnologías

Resumen

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.
Palabras clave: ARTIFICIAL NEURAL NETWORKS , IRRADIATION DAMAGE , KINETIC MONTE CARLO , MULTISCALE MODELLING
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
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URI: http://hdl.handle.net/11336/99256
URL: https://www.sciencedirect.com/science/article/pii/S0927025618301083
DOI: http://dx.doi.org/10.1016/j.commatsci.2018.02.025
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Citación
Castin, Nicolas; Pascuet, Maria Ines Magdalena; Messina, L.; Domain, C.; Olsson, P.; et al.; Advanced atomistic models for radiation damage in Fe-based alloys: Contributions and future perspectives from artificial neural networks; Elsevier; Computational Materials Science; 148; 6-2018; 116-130
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