Calculation of proper energy barriers for atomistic kinetic Monte Carlo simulations on rigid lattice with chemical and strain field long-range effects using artificial neural networks

摘要

In this paper we take a few steps further in the development of an approach based on the use of an artificial neural network (ANN) to introduce long-range chemical effects and zero temperature relaxation (elastic strain) effects in a rigid lattice atomistic kinetic Monte Carlo (AKMC) model. The ANN is trained to predict the vacancy migration energies as calculated given an interatomic potential with the nudged elastic band method, as functions of the local atomic environment. The kinetics of a single-vacancy migration is thus predicted as accurately as possible, within the limits of the given interatomic potential. The detailed procedure to apply this method is described and analyzed in detail. A novel ANN training algorithm is proposed to deal with the necessarily large number of input variables to be taken into account in the mathematical regression of the migration energies. The application of the ANN-based AKMC method to the simulation of a thermal annealing experiment in Fe–20%Cr alloy is reported. The results obtained are found to be in better agreement with experiments, as compared to already published simulations, where no atomic relaxation was taken into account and chemical effects were only heuristically allowed for. © 2010 American Institute of Physics Article Outline INTRODUCTION INFLUENCE OF LONG-RANGE INTERACTIONS ON ENERGY BARRIERS REGRESSION OF THE VACANCY MIGRATION ENERGIES IN ALLOYS WITH ANNs Generation of training and validation sets Neural network training algorithm APPLICATION TO BINARY AND TERNARY ALLOYS SIMULATIONS OF THE THERMAL ANNEALING OF FE–CR ALLOYS Thermodynamic consistency Description of the kinetics of the precipitation process CONCLUDING REMARKS