  A simulation can stand its ground against experiment only if its prediction
uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a
major source of prediction uncertainty, severely limiting the use of
large-scale classical atomistic simulations in a wide range of scientific and
engineering applications. Here we explore covariance between predictions of
metal plasticity, from 178 large-scale ($\sim 10^8$ atoms) molecular dynamics
(MD) simulations, and a variety of indicator properties computed at
small-scales ($\leq 10^2$ atoms). All simulations use the same 178 IPs. In a
manner similar to statistical studies in public health, we analyze correlations
of strength with indicators, identify the best predictor properties, and build
a cross-scale ``strength-on-predictors'' regression model. This model is then
used to quantify uncertainty over the statistical pool of IPs. Small-scale
predictors found to be highly covariant with strength are computed using
expensive quantum-accurate calculations and used to predict flow strength,
within the uncertainty bounds established in our statistical study.
