  White dwarfs (WDs) polluted by exoplanetary material provide the
unprecedented opportunity to directly observe the interiors of exoplanets.
However, spectroscopic surveys are often limited by brightness constraints, and
WDs tend to be very faint, making detections of large populations of polluted
WDs difficult. In this paper, we aim to increase considerably the number of WDs
with multiple metals in their atmospheres. Using 96,134 WDs with Gaia DR3 BP/RP
(XP) spectra, we constructed a 2D map using an unsupervised machine learning
technique called Uniform Manifold Approximation and Projection (UMAP) to
organize the WDs into identifiable spectral regions. The polluted WDs are among
the distinct spectral groups identified in our map. We have shown that this
selection method could potentially increase the number of known WDs with 5 or
more metal species in their atmospheres by an order of magnitude. Such systems
are essential for characterizing exoplanet diversity and geology.
