• DOI:10.1140/epjb/s10051-021-00261-1
• Corpus ID: 235683434

Machine learning S-wave scattering phase shifts bypassing the radial Schrödinger equation

@article{Romualdi2021MachineLS,
  title={Machine learning S-wave scattering phase shifts bypassing the radial Schr{\"o}dinger equation},
  author={Alessandro Romualdi and Gionni Marchetti},
  journal={The European Physical Journal B},
  year={2021},
  volume={94}
}

• A. Romualdi, Gionni Marchetti
• Published 25 June 2021
• Physics
• The European Physical Journal B

We present a proof of concept machine learning model resting on a convolutional neural network capable of yielding accurate scattering s-wave phase shifts caused by different three-dimensional spherically symmetric potentials at fixed collision energy thereby bypassing the radial Schrödinger equation. In our 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.