  Material characterization in nano-mechanical tests requires precise
interatomic potentials for the computation of atomic energies and forces with
near-quantum accuracy. For such purposes, we develop a robust neural-network
interatomic potential (NNIP), and we provide a test for the example of
molecular dynamics (MD) nanoindentation, and the case of body-centered cubic
crystalline molybdenum (Mo). We employ a similarity measurement protocol, using
standard local environment descriptors, to select ab initio configurations for
the training dataset that capture the behavior of the indented sample. We find
that it is critical to include generalized stacking fault (GSF) configurations,
featuring a dumbbell interstitial on the surface, to capture dislocation cores,
and also high-temperature configurations with frozen atom layers for the
indenter tip contact. We develop a NNIP with distinct dislocation nucleation
mechanisms, realistic generalized stacking fault energy (GSFE) curves, and an
informative energy landscape for the atoms on the sample surface during
nanoindentation. We compare our NNIP results with nanoindentation simulations,
performed with three existing potentials -- an embedded atom method (EAM)
potential, a gaussian approximation potential (GAP), and a tabulated GAP
(tabGAP) potential -- that predict different dislocation nucleation mechanisms,
and display the absence of essential information on the shear stress at the
sample surface in the elastic region. We believe that these features render
specialized NNIPs essential for simulations of nanoindentation and
nano-mechanics with near-quantum accuracy.
