  Neural network (NN) model chemistries (MCs) promise to facilitate the
accurate exploration of chemical space and simulation of large reactive
systems. One important path to improving these models is to add layers of
physical detail, especially long-range forces. At short range, however, these
models are data driven and data limited. Little is systematically known about
how data should be sampled, and `test data' chosen randomly from some sampling
techniques can provide poor information about generality. If the sampling
method is narrow `test error' can appear encouragingly tiny while the model
fails catastrophically elsewhere. In this manuscript we competitively evaluate
two common sampling methods: molecular dynamics (MD), normal-mode sampling
(NMS) and one uncommon alternative, Metadynamics (MetaMD), for preparing
training geometries. We show that MD is an inefficient sampling method in the
sense that additional samples do not improve generality. We also show MetaMD is
easily implemented in any NNMC software package with cost that scales linearly
with the number of atoms in a sample molecule. MetaMD is a black-box way to
ensure samples always reach out to new regions of chemical space, while
remaining relevant to chemistry near $k_bT$. It is one cheap tool to address
the issue of generalization.
