  Simulations of Al thin film sputter depositions rely on accurate plasma and
surface interaction models. Establishing the latter commonly requires a higher
level of abstraction and means to dismiss the fundamental atomic fidelity.
Previous works on sputtering processes addressed this issue by establishing
machine learning surrogate models, which include a basic surface state (i.e.,
stoichiometry) as static input. In this work, an evolving surface state and
defect structure are introduced to jointly describe sputtering and growth with
physics-separating artificial neural networks. The data describing the
plasma-surface interactions stem from hybrid reactive molecular
dynamics/time-stamped force bias Monte Carlo simulations of Al neutrals and
Ar$^+$ ions impinging onto Al(001) surfaces. It is demonstrated that the
fundamental processes are comprehensively described by taking the surface state
as well as defect structure into account. Hence, a machine learning
plasma-surface interaction surrogate model is established that resolves the
inherent kinetics with high physical fidelity. The resulting model is not
restricted to input from modeling and simulation, but may similarly be applied
to experimental input data.
