  Bottom-up coarse-grained molecular dynamics models are parameterized using
complex effective Hamiltonians. These models are typically optimized to
approximate high dimensional data from atomistic simulations. In contrast,
human validation of these models is often limited to low dimensional statistics
that do not necessarily differentiate between the CG model and said atomistic
simulations. We propose that explainable machine learning can directly convey
high-dimensional error to scientists and use Shapley additive explanations do
so in two coarse-grained protein models.
