  Kilonovae are likely a key site of heavy r-process element production in the
Universe, and their optical/infrared spectra contain insights into both the
properties of the ejecta and the conditions of the r-process. However, the
event GW170817/AT2017gfo is the only kilonova so far with well-observed
spectra. To understand the diversity of absorption features that might be
observed in future kilonovae spectra, we use the TARDIS Monte Carlo radiative
transfer code to simulate a suite of optical spectra spanning a wide range of
kilonova ejecta properties and r-process abundance patterns. To identify the
most common and prominent absorption lines, we perform dimensionality reduction
using an autoencoder, and we find spectra clusters in the latent space
representation using a Bayesian Gaussian Mixture model. Our synthetic kilonovae
spectra commonly display strong absorption by strontium Sr II, yttrium Y II,
and zirconium Zr I - II, with strong lanthanide contributions at low electron
fractions (Ye < 0.25). When a new kilonova is observed, our machine learning
framework will provide context on the dominant absorption lines and key ejecta
properties, helping to determine where this event falls within the larger 'zoo'
of kilonovae spectra.
