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We present a proof of concept machine learning model resting on aconvolutional neural network capable to yield accurate scattering s-wave phaseshifts caused by different three-dimensional spherically symmetric potentialsat fixed collision energy thereby bypassing the radial Schr\"{o}dingerequation. In out work, we discuss how the Hamiltonian can serve as a guidingprinciple in the construction of a physically-motivated descriptor. The goodperformance, even in presence of bound states in the data sets, exhibited byour model that accordingly is trained on the Hamiltonian through eachscattering potential, demonstrates the feasibility of this proof of principle.

Machine Learning S-Wave Scattering Phase Shifts Bypassing the Radial Schr\"odinger Equation