Efficient emulation of nuclear ground states with neural-network variational Monte Carlo and eigenvector continuation

Abstract

An efficient emulator for \emph{ab initio} calculations of nuclear ground-state properties is developed by integrating the neural-network variational Monte Carlo framework, FeynmanNet, with the eigenvector continuation. It enables the calculation of observables for different Hamiltonians with minimal computational cost, while delivering ground-state energies with errors below $0.5\%$ compared to the full FeynmanNet results. With this emulator, the ground-state energies and charge radii of ${}^{16}\mathrm{O}$, ${}^{15}\mathrm{O}$, ${}^{14}\mathrm{O}$, ${}^{15}\mathrm{N}$, and ${}^{14}\mathrm{C}$ are computed using a nuclear Hamiltonian derived from the leading-order pionless effective field theory, with a large number of different values of low-energy constants (LECs). Then, we perform a global sensitivity analysis of the ground-state energies, charge radii, separation energies of selected nuclei for the three LECs in the Hamiltonian, to identify how each LEC contributes to the variances of these observables. It shows that the two-body LEC in the $^3S_1$ channel is the most influential LEC governing these nuclear bulk properties. Finally, the correlations among the ground-state energies of $^4$He, $^{12}$C, and $^{16}$O are investigated by varying the LECs in the Hamiltonian. The analysis reveals that the experimental ground-state energies of $^{12}$C and $^{16}$O cannot be reproduced simultaneously by varying the LECs in the leading-order pionless Hamiltonian. This suggests that additional ingredients in the leading-order Hamiltonian are required to improve its description of light nuclei. The present work establishes an efficient framework for global sensitivity analysis and uncertainty quantification in the quantum Monte Carlo calculations for light and medium-mass nuclei.