  The reliability of atomistic simulations depends on the quality of the
underlying energy models providing the source of physical information, for
instance for the calculation of migration barriers in atomistic Kinetic Monte
Carlo simulations. Accurate (high-fidelity) methods are often available, but
since they are usually computationally expensive, they must be replaced by less
accurate (low-fidelity) models that introduce some degrees of approximation.
Machine-learning techniques such as artificial neural networks are usually
employed to work around this limitation and extract the needed parameters from
large databases of high-fidelity data, but the latter are often computationally
expensive to produce. This work introduces an alternative method based on the
multifidelity approach, where correlations between high-fidelity and
low-fidelity outputs are exploited to make an educated guess of the
high-fidelity outcome based only on quick low-fidelity estimations, hence
without the need of running full expensive high-fidelity calculations. With
respect to neural networks, this approach is expected to require less training
data because of the lower amount of fitting parameters involved. The method is
tested on the prediction of ab initio formation and migration energies of
vacancy diffusion in iron-copper alloys, and compared with the neural networks
trained on the same database.
