TY - JOUR
T1 - Prediction of point-defect migration energy barriers in alloys using artificial intelligence for atomistic kinetic Monte Carlo applications
AU - Castin, Nicolas
AU - Malerba, Lorenzo
A2 - Bonny, Giovanni
N1 - Score = 10
PY - 2009/6
Y1 - 2009/6
N2 - We significantly improved a previously proposed method to take into account chemical and also relaxation
effects on point-defect migration energy barriers, as predicted by an interatomic potential, in a rigid
lattice atomistic kinetic Monte Carlo simulation. Examples of energy barriers are rigorously calculated,
including chemical and relaxation effects, as functions of the local atomic configuration, using a nudged
elastic bands technique. These examples are then used to train an artificial neural network that provides
the barriers on-demand during the simulation for each configuration encountered by the migrating
defect. Thanks to a newly developed training method, the configuration can include a large number of
neighbour shells, thereby properly including also strain effects. Satisfactory results have been obtained
when the configuration includes different chemical species only. The problems encountered in the extension
of the method to configurations including any number of point-defects are stated and solutions to
tackle them are sketched.
AB - We significantly improved a previously proposed method to take into account chemical and also relaxation
effects on point-defect migration energy barriers, as predicted by an interatomic potential, in a rigid
lattice atomistic kinetic Monte Carlo simulation. Examples of energy barriers are rigorously calculated,
including chemical and relaxation effects, as functions of the local atomic configuration, using a nudged
elastic bands technique. These examples are then used to train an artificial neural network that provides
the barriers on-demand during the simulation for each configuration encountered by the migrating
defect. Thanks to a newly developed training method, the configuration can include a large number of
neighbour shells, thereby properly including also strain effects. Satisfactory results have been obtained
when the configuration includes different chemical species only. The problems encountered in the extension
of the method to configurations including any number of point-defects are stated and solutions to
tackle them are sketched.
KW - Artificial intelligence
KW - atomistic kinetic Monte Carlo
KW - chemical and relaxation effects
UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_101742
UR - http://knowledgecentre.sckcen.be/so2/bibref/6335
U2 - 10.1016/j.nimb.2009.06.041
DO - 10.1016/j.nimb.2009.06.041
M3 - Article
VL - 267
SP - 3148
EP - 3151
JO - Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
JF - Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
T2 - 2008 - COSIRES
Y2 - 12 October 2008 through 17 October 2008
ER -