  Simulations of high energy density physics are expensive in terms of
computational resources. In particular, the computation of opacities of plasmas
in the non-local thermal equilibrium (NLTE) regime can consume as much as 90\%
of the total computational time of radiation hydrodynamics simulations for high
energy density physics applications. Previous work has demonstrated that a
combination of fully-connected autoencoders and a deep jointly-informed neural
network (DJINN) can successfully replace the standard NLTE calculations for the
opacity of krypton. This work expands this idea to combining multiple elements
into a single surrogate model with the focus here being on the autoencoder.
