Improved atomistic Monte Carlo models based on ab-initio -trained neural networks: Application to FeCu and FeCr alloys-Reference-Cited by-同舟云学术

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Improved atomistic Monte Carlo models based on ab-initio -trained neural networks: Application to FeCu and FeCr alloys

Published:2017-06-29 Issue:21 Volume:95 Page:
ISSN:2469-9950
Container-title:Physical Review B
language:en
Short-container-title:Phys. Rev. B

Author:

Castin N.,Messina L.,Domain C.,Pasianot R. C.,Olsson P.

Funder

Horizon 2020 Framework Programme

FP7 Nuclear Fission, Safety and Radiation Protection

Vattenfall

Seventh Framework Programme

Consejo Nacional de Investigaciones Científicas y Técnicas

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevB.95.214117/fulltext

Reference63 articles.

1. A new algorithm for Monte Carlo simulation of Ising spin systems

2. Kinetic pathways from embedded-atom-method potentials: Influence of the activation barriers

3. Homogeneous phase separation in binary alloys under ion irradiation conditions: Role of interstitial atoms

4. Early stages ofα−α′phase separation in Fe-Cr alloys: An atomistic study

5. Multiscale modelling of defect kinetics in irradiated iron

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