  Modern applications of atomic physics, including the determination of
frequency standards, and the analysis of astrophysical spectra, require
prediction of atomic properties with exquisite accuracy. For complex atomic
systems, high-precision calculations are a major challenge due to the
exponential scaling of the involved electronic configuration sets. This
exacerbates the problem of required computational resources for these
computations, and makes indispensable the development of approaches to select
the most important configurations out of otherwise intractably huge sets. We
have developed a neural network (NN) tool for running high-precision atomic
configuration interaction (CI) computations with iterative selection of the
most important configurations. Integrated with the established pCI atomic
codes, our approach results in computations with significantly reduced
computational requirements in comparison with those without NN support. We
showcase a number of NN-supported computations for the energy levels of
Fe$^{16+}$ and Ni$^{12+}$, and demonstrate that our approach can be reliably
used and automated for solving specific computational problems for a wide
variety of systems.
