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Calculation of migration rates of vacancies and divacancies in α-Iron using transition path sampling biased with a Lyapunov indicator

Published online by Cambridge University Press:  07 February 2012

Massimiliano Picciani ,
Manuel Athènes  and
Mihai-Cosmin Marinica
Massimiliano Picciani
Affiliation:
CEA, DEN, Service de Recherches de Métallurgie Physique, F-91191 Gif-sur-Yvette, France
Manuel Athènes
Affiliation:
CEA, DEN, Service de Recherches de Métallurgie Physique, F-91191 Gif-sur-Yvette, France
Mihai-Cosmin Marinica
Affiliation:
CEA, DEN, Service de Recherches de Métallurgie Physique, F-91191 Gif-sur-Yvette, France
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Abstract

Predicting the microstructural evolution of radiation damage in materials requires handling the physics of infrequent-events, in which several time scales are involved. The reactions rates characterizing these events are the main ingredient for simulating the kinetics of materials under irradiation over large time scales and high irradiation doses. We propose here an efficient, finite temperature method to compute reaction rate constants of thermally activated processes. The method consists of two steps. Firstly, rare reactive trajectories in phase-space are sampled using a transition path sampling (TPS) algorithm supplemented with a local Lyapunov bias favoring diverging trajectories. This enables the system to visit transition regions separating stable configurations more often, and thus enhances the probability of observing transitions between stable states during relatively short simulations. Secondly, reaction constants are estimated from the unbiased fraction of reactive trajectories, yielded by an appropriate statistical data analysis tool, the multistate Bennett acceptance ratio (MBAR) package. We apply our method to the calculation of reaction rates for vacancy and di-vacancy migration in α-Iron crystal, using an Embedded Atom Model potential, for temperatures ranging from 300 K to 800 K.

Keywords

simulationdiffusiondefects
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1383: Symposium A – Material Challenges in Current and Future Nuclear Technologies , 2012 , mrsf11-1383-a05-05
Copyright
Copyright © Materials Research Society 2012

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References

REFERENCES

1. Frenkel, D., Smit, B., Understanding molecular simulation (Academic Press, London, 2002)Google Scholar
2. Dellago, C., Bolhuis, P.G., Chandler, D., J. Chem. Phys. 110, 6617 (1999).Google Scholar
3. Shirts, M.R., Chodera, J.D., J. Chem. Phys. 129, 124105 (2008).Google Scholar
4. Picciani, M., arXiv:1111.1568v1 (preprint)Google Scholar
5. Marinica, M.C., Willaime, F., Mousseau, N., Phys. Rev. B 83, 094119 (2011).Google Scholar
6. Hänggi, P., Talkner, P., Borkovec, M., Rev. Mod. Phys. 62, 251 (1990).Google Scholar
7. Vineyard, G.H., J. Phys. Chem. Sol. 3, 121 (1957).Google Scholar
8. Ackland, G., Medelev, M., Srolovitz, D., Han, S., Barashev, A., J. Phys.: Condens. Matter 16, 2629 (2004).Google Scholar
9. Fu, C.C., Dalla Torre, J., Willaime, F., Barbu, A., Bocquet, J.L., Nature Mat. 4, 67 (2005).Google Scholar