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Machine learning the deuteron

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arxiv 1911.13092 v2 pith:LEYKVHJG submitted 2019-11-29 nucl-th physics.comp-ph

J. W. T. Keeble , A. Rios This is my paper
classification nucl-thphysics.comp-ph
keywords deuteronlearningmachinenetworkproblemexactneuralnuclear
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Meson-Nucleus Bound States with Neural-Network Quantum States

    hep-ph 2026-06 unverdicted novelty 7.0 of 10

    Neural-network quantum states applied to HAL QCD meson-nucleon potentials predict bound states for phi at A>=2, J/psi at A>=4, and eta_c at A>=6, with binding energies from tens of MeV to sub-MeV scales.

  2. Medium-mass nuclei with neural quantum states

    nucl-th 2026-07 conditional novelty 6.5 of 10

    Pfaffian-Jastrow neural quantum states yield ground-state energies and charge radii for nuclei up to A=58, with weak p-wave terms reducing average energy error to ~3% while revealing Hamiltonian sensitivity and A^3 scaling.

  3. Fully-heavy multiquarks in neural-network quantum states

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    Neural-network quantum states are used to compute spectra of fully-heavy multiquarks in a non-relativistic quark model, claiming to overcome dimensionality issues with superior accuracy over prior approximations.

  4. Neural-network solution of subtracted three-body Faddeev integral equations near the Efimov limit

    nucl-th 2026-06 unverdicted novelty 5.0 of 10

    A DNN solves the symmetrized spectator form of the subtracted Faddeev equations for three identical bosons, reproducing Efimov binding scales at unitarity to within 0.022% and tracing bound-state branches versus inver...

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