Extrapolation to infinite model space of no-core shell model calclulations using machine learning

An ensemble of neural networks is employed to extrapolate no-core shell model (NCSM) results to infinite model space for light nuclei. We present a review of our neural network extrapolations of the NCSM results obtained with the Daejeon16 N N interaction in different model spaces and with different values of the NCSM basis parameter ħΩ for energies of nuclear states and root-mean-square (rms) radii of proton, neutron and matter distributions in light nuclei. The method yields convergent predictions with quantifiable uncertainties. Ground-state energies for 6 Li, 6 He, and the unbound 6 Be, as well as the excited (3 + , 0) and (0 + , 1) states of 6 Li, are obtained within a few hundred keV of experiment. The extrapolated radii of bound states converge well In contrast, radii of unbound states in 6 Be and 6 Li do not stabilize. These results demonstrate that machinelearning extrapolations can extend the reach of ab initio nuclear structure calculations with reliable accuracy.