DFT-Parameterized Object Kinetic Monte Carlo Simulations of Radiation Damage

  • Enrique MartínezEmail author
  • María José Caturla
  • Jaime Marian
Living reference work entry


Materials for nuclear applications are subjected to extremely stringent conditions. The incoming energetic particles create different types of defects in the material that modify the system microstructure. These defects diffuse and interact with each other and pre-existing features in the material, leading to alterations of the material properties, and even to failure. To deploy reliable materials for such extreme applications, a deep understanding of the microstructural changes and their relation to material properties is critically required. Synergistic experimental and theoretical studies are paramount to gain such crucial knowledge. In this work we review one theoretical venue developed over the years to first understand and then predict the material response upon irradiation: an object kinetic Monte Carlo (OKMC) approach parameterized to first-principles data. We review the theory behind the kinetic Monte Carlo (KMC) algorithm and the specifics of the OKMC as a mesoscale methodology. We describe density functional theory (DFT) as an ab initio approach that can accurately calculate parameters required by the OKMC as input data to be able to analyze the microstructure evolution of the system. Finally, we show two applications lengthily studied in the literature: the microstructural evolution of both ferritic steels and tungsten under diverse irradiation conditions.



The authors thank Malvin H. Kalos, Alfredo Caro, Frédéric Soisson, Vasily Bulatov, Blas Uberuaga, Arthur F. Voter, and Danny Perez for many useful discussions. E.M. wants to thank Gustavo Esteban for useful comments on the manuscript. E.M. acknowledges the support of the U.S. DOE, Office of Science, Advanced Scientific Computing Research and Fusion Energy Sciences through the Scientific Discovery through Advanced Computing (SciDAC) project on Plasma-Surface Interactions for this work. This research used resources provided by the LANL Institutional Computing Program. LANL, an affirmative action/equal opportunity employer, is operated by Los Alamos National Security, LLC, for the National Nuclear Security Administration of the US DOE under contract DE-AC52-06NA25396.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Enrique Martínez
    • 1
    Email author
  • María José Caturla
    • 2
  • Jaime Marian
    • 3
    • 4
  1. 1.Material Science and Technology Division, MST-8Los Alamos National LaboratoryLos AlamosUSA
  2. 2.Facultad de Ciencias, Fase II, Department of Física AplicadaUniversidad de AlicanteAlicanteSpain
  3. 3.Department of Materials Science and EngineeringUniversity of CaliforniaLos AngelesUSA
  4. 4.Department of Mechanical and Aerospace EngineeringUniversity of CaliforniaLos AngelesUSA

Section editors and affiliations

  • Michael P. Short
    • 1
  • Kai Nordlund
    • 2
  1. 1.Department of Nuclear Science and EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Computational Materials PhysicsUniversity of HelsinkiHelsinkiFinland

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