  The hunt for dark matter remains one of the principal objectives of modern
physics and cosmology. Searches for dark matter in the form of axions are
proposed or underway across a range of experimental collaborations. As we look
to the next generation of detectors, a natural question to ask is whether there
are new experimental designs waiting to be discovered and how we might find
them. Here we take a new approach to the experimental design procedure by using
gradient descent techniques to search for optimal detector designs. We provide
a proof of principle for this technique by searching 1D detectors varying the
bulk properties of the detector until the optimal detector design is obtained.
Remarkably, we find the detector is capable of out-performing a human designed
experiment on which the search was initiated. This opens the door to further
gradient descent searches of more complex 2D and 3D designs across a wider
variety of materials and boundary geometries of the detector. There is also an
opportunity to use more sophisticated gradient descent algorithms to complete a
more exhaustive scan of the landscape of designs.
