  We present a proof of concept machine learning model resting on a
convolutional neural network capable to yield accurate scattering s-wave phase
shifts caused by different three-dimensional spherically symmetric potentials
at fixed collision energy thereby bypassing the radial Schr\"{o}dinger
equation. In out work, we discuss how the Hamiltonian can serve as a guiding
principle in the construction of a physically-motivated descriptor. The good
performance, even in presence of bound states in the data sets, exhibited by
our model that accordingly is trained on the Hamiltonian through each
scattering potential, demonstrates the feasibility of this proof of principle.
