  Hydrogen incorporation in native surface oxides of metal alloys often
controls the onset of metal hydriding, with implications for materials
corrosion and hydrogen storage. A key representative example is titania, which
forms as a passivating layer on a variety of titanium alloys for structural and
functional applications. These oxides tend to be structurally diverse,
featuring polymorphic phases, grain boundaries, and amorphous regions that
generate a disparate set of unique local environments for hydrogen. Here, we
introduce a workflow that can efficiently and accurately navigate this
complexity. First, a machine learning force field, trained on ab initio
molecular dynamics simulations, was used to generate amorphous configurations.
Density functional theory calculations were then performed on these structures
to identify local oxygen environments, which were compared against experimental
observations. Second, to classify subtle differences across the disordered
configuration space, we employ a graph-based sampling procedure. Finally, local
hydrogen binding energies are computed using exhaustive density functional
theory calculations on representative configurations. We leverage this
methodology to show that hydrogen binding energetics are described by local
oxygen coordination, which in turn is affected by stoichiometry. Together these
results imply that hydrogen incorporation and transport in TiO$_x$ can be
tailored through compositional engineering, with implications for improving
performance and durability of titanium-derived alloys in hydrogen environments.
