A neural network approach to running high-precision atomic computations

TL;DR

This paper introduces a neural network method integrated with atomic codes to efficiently select important configurations, enabling high-precision atomic property calculations with reduced computational resources.

Contribution

The authors developed a neural network tool that enhances atomic configuration interaction computations by iteratively selecting key configurations, improving efficiency and automation.

Findings

Reduced computational requirements for atomic calculations.

Successful application to Fe$^{16+}$ and Ni$^{12+}$ energy levels.

Demonstrated reliability and automation potential of the approach.

Abstract

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.