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Advanced atomistic models for radiation damage in Fe-based alloys: Contributions and future perspectives from artificial neural networks
Studie Ctr Kerneenergie, Ctr Etud Energie Nucl SCK CEN, NMS Unit, Boeretang 200, B-2400 Mol, Belgium..
Consejo Nacl Invest Cient & Tecn CONICET, Godoy Cruz 2290 C1425FQB CABA, Buenos Aires, DF, Argentina..
KTH, School of Engineering Sciences (SCI), Physics, Reactor Physics.ORCID iD: 0000-0003-0562-9070
EDF R&D, Dept Mat & Mecan Composants, F-77250 Moret Sur Loing, France..
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2018 (English)In: Computational materials science, ISSN 0927-0256, E-ISSN 1879-0801, Vol. 148, p. 116-130Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 148, p. 116-130
Keywords [en]
Artificial neural networks, Kinetic Monte Carlo, Irradiation damage, Multiscale modelling
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:kth:diva-226734DOI: 10.1016/j.commatsci.2018.02.025ISI: 000428907600013Scopus ID: 2-s2.0-85042355717OAI: oai:DiVA.org:kth-226734DiVA, id: diva2:1203316
Funder
EU, Horizon 2020, 755039, 661913
Note

QC 20180503

Available from: 2018-05-03 Created: 2018-05-03 Last updated: 2018-05-03Bibliographically approved

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Messina, LucaOlsson, Pär

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