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

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

URL: http://arxiv.org/abs/2106.16152v6
Date: Fri, 11 Feb 2022 23:12:52 GMT
Title: Machine Learning S-Wave Scattering Phase Shifts Bypassing the Radial Schr\"odinger Equation
Authors: Alessandro Romualdi and Gionni Marchetti
Abstract summary: We present a proof of concept machine learning model resting on a convolutional neural network capable to yield accurate scattering s-wave phase shifts. We discuss how the Hamiltonian can serve as a guiding principle in the construction of a physically-motivated descriptor.
Score: 77.34726150561087
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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