the results in the infinite matrix limit.'
author:
- '\'
bibliography:
- 'bibtemplate\_ANN\_Nuclear\_Physics\_2018\_short.bib'
title: |
    **Deep Learning: A Tool for Computational Nuclear Physics**\
    \[0.2ex\]
---

Nuclear structure of $^6Li$; ab initio no-core shell model; ground state energy; point proton root-mean-square radius; artificial neural network.

Introduction {#sec:Introduction}
============

Nuclei are complicated quantum many-body systems, whose inter-nucleon interactions are not known precisely. The goal of ab initio nuclear theory is to accurately describe nuclei from the first principles as systems of nucleons that interact by fundamental interactions. With sufficiently precise many-body tools, we learn important features of these interactions, such as the fact that three-nucleon (NNN) interactions are critical for understanding the anomalous long lifetime of $^{14}\mathrm{C}$ [@PhysRevLett.106.202502]. With access to powerful High Performance Computing (HPC) systems, several ab initio approaches have been developed to study nuclear structure and reactions, such as the No-Core Shell Model (NCSM) [@NCSM:2013], the Green’s Function Monte Carlo (GFMC) [@MonteCarlo:2001], the Coupled-Cluster Theory (CC) [@CoupledCluster:2004], the Hyperspherical expansion method [@LEIDEMANN:2013158], the Nuclear Lattice Effective Field Theory [@LEE:2009117][@PhysRevLett.106.192501], the No-Core Shell Model with Continuum [@NCSM:2013] and the NCSM-SS-HORSE approach [@PhysRevC.94.064320]. These approaches have proven to be successful in reproducing the experimental nuclear spectra for a small fraction of 