  We present a scale-bridging approach based on a multi-fidelity (MF)
machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse
atomistic computational model predictions across multiple levels of fidelity.
Through the posterior variance of the MFGP, our framework naturally enables
uncertainty quantification, providing estimates of confidence in the
predictions. We used Density Functional Theory as high-fidelity prediction,
while a ML interatomic potential is used as the low-fidelity prediction.
Practical materials design efficiency is demonstrated by reproducing the
ternary composition dependence of a quantity of interest (bulk modulus) across
the full aluminum-niobium-titanium ternary random alloy composition space. The
MFGP is then coupled to a Bayesian optimization procedure and the computational
efficiency of this approach is demonstrated by performing an on-the-fly search
for the global optimum of bulk modulus in the ternary composition space. The
framework presented in this manuscript is the first application of MFGP to
atomistic materials simulations fusing predictions between Density Functional
Theory and classical interatomic potential calculations.
