  Understanding and modeling plasma-surface interactions frame a multi-scale as
well as multi-physics problem. Scale-bridging machine learning surface
surrogate models have been demonstrated to perceive the fundamental atomic
fidelity for the physical vapor deposition of pure metals. However, the immense
computational cost of the data-generating simulations render a practical
application with predictions on relevant timescales impracticable. This issue
is resolved in this work for the sputter deposition of AlN in Ar/N$_2$
discharges by developing a scheme that populates the parameter spaces
effectively. Hybrid reactive molecular dynamics / time-stamped force-bias Monte
Carlo simulations of randomized plasma-surface interactions / diffusion
processes are used to setup a physics-separating artificial neural network. The
application of this generic machine learning model to a specific experimental
reference case study enables the systematic analysis of the particle flux
emission as well as underlying system state (e.g., composition, mass density,
stress, point defect structure) evolution within process times of up to 45
minutes.
