  Machine learning has become a premier tool in physics and other fields of
science. It has been shown that the quantum mechanical scattering problem can
not only be solved with such techniques, but it was argued that the underlying
neural network develops the Born series for shallow potentials. However,
classical machine learning algorithms fail in the unitary limit of an infinite
scattering length and vanishing effective range parameters. The unitary limit
plays an important role in our understanding of bound strongly interacting
fermionic systems and can be realized in cold atom experiments. Here, we
develop a formalism that explains the unitary limit in terms of what we define
as unitary limit surfaces. This not only allows to investigate the unitary
limit geometrically in potential space, but also provides a numerically simple
approach towards unnaturally large scattering lengths with standard multilayer
perceptrons. Its scope is therefore not limited to applications in nuclear and
atomic physics, but includes all systems that exhibit an unnaturally large
scale.
