University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Machine learning the deuteron

Keeble, J.W.T. and Rios, A. (2020) Machine learning the deuteron Physics Letters, Section B, 809, 135743.

1-s2.0-S0370269320305463-main.pdf - Version of Record

Download (849kB) | Preview


We use machine learning techniques to solve the nuclear two-body bound state problem, the deuteron. We use a minimal one-layer, feed-forward neural network to represent the deuteron S- and D-state wavefunction in momentum space, and solve the problem variationally using ready-made machine learning tools. We benchmark our results with exact diagonalisation solutions. We find that a network with 6 hidden nodes (or 24 parameters) can provide a faithful representation of the ground state wavefunction, with a binding energy that is within 0.1% of exact results. This exploratory proof-of principle simulation may provide insight for future potential solutions of the nuclear many-body problem using variational artificial neural network techniques.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Physics
Authors :
Date : 10 September 2020
Funders : Science and Technology Facilities Council (STFC)
DOI : 10.1016/j.physletb.2020.135743
Copyright Disclaimer : © 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( Funded by SCOAP3.
Uncontrolled Keywords : Deuteron; Quantum many-body theory; Machine learning Neural networks
Depositing User : Diane Maxfield
Date Deposited : 01 Oct 2020 12:59
Last Modified : 11 Oct 2020 02:08

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800