Modeling Radiation-Induced Segregation and Precipitation: Contributions and Future Perspectives from Artificial Neural Networks

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Modeling Radiation-Induced Segregation and Precipitation: Contributions and Future Perspectives from Artificial Neural Networks. / Castin, Nicolas; Malerba, Lorenzo.

Handbook of Materials Modeling. 2019. ed. Switzerland : Springer, 2018. p. 1-22.

Research output: Contribution to report/book/conference proceedingsChapter

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@inbook{20e6597accf748339bfd67f78e9e302c,
title = "Modeling Radiation-Induced Segregation and Precipitation: Contributions and Future Perspectives from Artificial Neural Networks",
abstract = "Radiation-induced segregation and precipitation is one of the main responsibles for the changes in macroscopic properties taking place in steels under irradiation. For instance, reactor pressure vessel steels and ferritic-martensitic steels are known to harden and embrittle within the lifetime of the reactors. The development of quantitative predictive models for these changes is a very challenging task, because of the highly multiscale nature of the mechanisms taking place at the microstructural level and the chemical complexities involved. Fully physically based approaches such as object kinetic Monte Carlo models are promising tools to take this challenge, but their parametrization must be accurately and adequately elaborated. In this chapter, we revise how highly powerful and flexible numerical tools offered by machine learning systems, specifically artificial neural networks, have contributed to these models. Perspectives for future developments are also discussed,",
keywords = "modelling, Artificial Intelligence, Kinetic Monte Carlo, atomic simulation",
author = "Nicolas Castin and Lorenzo Malerba",
note = "Score=10",
year = "2018",
month = "9",
day = "7",
doi = "10.1007/978-3-319-50257-1_140-1",
language = "English",
pages = "1--22",
booktitle = "Handbook of Materials Modeling",
publisher = "Springer",
edition = "2019",

}

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TY - CHAP

T1 - Modeling Radiation-Induced Segregation and Precipitation: Contributions and Future Perspectives from Artificial Neural Networks

AU - Castin, Nicolas

AU - Malerba, Lorenzo

N1 - Score=10

PY - 2018/9/7

Y1 - 2018/9/7

N2 - Radiation-induced segregation and precipitation is one of the main responsibles for the changes in macroscopic properties taking place in steels under irradiation. For instance, reactor pressure vessel steels and ferritic-martensitic steels are known to harden and embrittle within the lifetime of the reactors. The development of quantitative predictive models for these changes is a very challenging task, because of the highly multiscale nature of the mechanisms taking place at the microstructural level and the chemical complexities involved. Fully physically based approaches such as object kinetic Monte Carlo models are promising tools to take this challenge, but their parametrization must be accurately and adequately elaborated. In this chapter, we revise how highly powerful and flexible numerical tools offered by machine learning systems, specifically artificial neural networks, have contributed to these models. Perspectives for future developments are also discussed,

AB - Radiation-induced segregation and precipitation is one of the main responsibles for the changes in macroscopic properties taking place in steels under irradiation. For instance, reactor pressure vessel steels and ferritic-martensitic steels are known to harden and embrittle within the lifetime of the reactors. The development of quantitative predictive models for these changes is a very challenging task, because of the highly multiscale nature of the mechanisms taking place at the microstructural level and the chemical complexities involved. Fully physically based approaches such as object kinetic Monte Carlo models are promising tools to take this challenge, but their parametrization must be accurately and adequately elaborated. In this chapter, we revise how highly powerful and flexible numerical tools offered by machine learning systems, specifically artificial neural networks, have contributed to these models. Perspectives for future developments are also discussed,

KW - modelling

KW - Artificial Intelligence

KW - Kinetic Monte Carlo

KW - atomic simulation

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/31626701

U2 - 10.1007/978-3-319-50257-1_140-1

DO - 10.1007/978-3-319-50257-1_140-1

M3 - Chapter

SP - 1

EP - 22

BT - Handbook of Materials Modeling

PB - Springer

CY - Switzerland

ER -

ID: 4740034