Neural-network approach to running high-precision atomic computations
Pavlo Bilous, Charles Cheung, and Marianna Safronova
Phys. Rev. A 110, 042818 (2024) - Published 16 October, 2024
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 and and demonstrate that our approach can be reliably used and automated for solving specific computational problems for a wide variety of systems.
