Abstract
We propose a methodology for predicting migration energies associated to the migration of single atoms
towards vacant sites, using artificial neural networks. The novelty of the approach, which has already
been proven efficient for bulk materials (e.g. bcc or fcc Fe-based alloys), is to allow for any structure,
without restriction to a specific lattice. The proposed technique is designed in conjunction with a novel
kind of lattice-free atomistic kinetic Monte Carlo model. The idea is to avoid as much as possible heavy
atomistic simulations, e.g. static relaxation or general methods for finding transition paths. Such calculations,
however, are applied once per Monte Carlo event, when a selected event is applied. The objective of
this work is thus to propose a methodology for defining migration events at every step of the simulation,
and at the same time assigning a frequency of occurrence to them (using artificial neural networks), in
short computing times. We demonstrate the feasibility of this new concept by designing neural networks
for predicting vacancy migration energies near grain boundaries in bcc FeCr alloys.
Details
Original language | English |
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Pages (from-to) | 217-225 |
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Journal | Computational Materials Science |
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Volume | 84 |
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DOIs | |
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Publication status | Published - 5 Dec 2013 |
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