  We train a deep neural network (DNN) to output rates of dark matter (DM)
induced electron excitations in silicon and germanium detectors. Our DNN
provides a massive speedup of around $5$ orders of magnitude relative to
existing methods (i.e. QEdark-EFT), allowing for extensive parameter scans in
the event of an observed DM signal. The network is also lighter and simpler to
use than alternative computational frameworks based on a direct calculation of
the DM-induced excitation rate. The DNN can be downloaded
$\href{https://github.com/urdshals/DEDD}{\text{here}}$.
