TY - JOUR
T1 - Atomistic Kinetic Monte Carlo studies of microchemical evolutions driven by diffusion processes under irradiation
AU - Soisson, Frédéric
AU - Becquart, Charlotte
AU - Castin, Nicolas
AU - Domain, Christophe
AU - Malerba, Lorenzo
AU - Vincent, Edwige
A2 - Terentyev, Dmitry
A2 - Bonny, Giovanni
N1 - Score = 10
PY - 2010/11
Y1 - 2010/11
N2 - Atomistic Kinetic Monte Carlo (AKMC) simulations are a powerful tool to study the microstructural and microchemical evolution of alloys controlled by diffusion processes, under irradiation and during thermal ageing. In the framework of the FP6 Perfect program, two main approaches have been applied to binary and multicomponent iron based alloys. The first one is based on a diffusion model which takes into account vacancy and self-interstitial jumps, using simple rigid lattice approximation and broken-bond models to compute the point-defect jump frequencies. The corresponding parameters are fitted on ab initio calculations of a few typical configurations and migration barriers. The second method uses empirical potentials to compute a much larger number of migration barriers, including atomic relaxations, and Artificial Intelligence regression methods to predict the other ones. It is somewhat less rapid than the first one, but significantly more than simulations using ‘‘on-the-fly” calculations of all the barriers. We review here the recent advances and perspectives concerning these techniques.
AB - Atomistic Kinetic Monte Carlo (AKMC) simulations are a powerful tool to study the microstructural and microchemical evolution of alloys controlled by diffusion processes, under irradiation and during thermal ageing. In the framework of the FP6 Perfect program, two main approaches have been applied to binary and multicomponent iron based alloys. The first one is based on a diffusion model which takes into account vacancy and self-interstitial jumps, using simple rigid lattice approximation and broken-bond models to compute the point-defect jump frequencies. The corresponding parameters are fitted on ab initio calculations of a few typical configurations and migration barriers. The second method uses empirical potentials to compute a much larger number of migration barriers, including atomic relaxations, and Artificial Intelligence regression methods to predict the other ones. It is somewhat less rapid than the first one, but significantly more than simulations using ‘‘on-the-fly” calculations of all the barriers. We review here the recent advances and perspectives concerning these techniques.
KW - atomistic kinetic Monte Carlo
KW - artificial intelligence
UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_108516
UR - http://knowledgecentre.sckcen.be/so2/bibref/7253
U2 - 10.1016/j.jnucmat.2010.05.018
DO - 10.1016/j.jnucmat.2010.05.018
M3 - Article
SN - 0022-3115
VL - 406
SP - 55
EP - 67
JO - Journal of Nuclear Materials
JF - Journal of Nuclear Materials
IS - 1
ER -