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Q: Fachverband Quantenoptik und Photonik
Q 17: Precision Spectroscopy of Atoms and Ions II (joint session A/Q)
Q 17.4: Talk
Monday, March 10, 2025, 18:00–18:15, HS PC
Neural-network approach to large atomic structure computations with pCI and other atomic codes — •Pavlo Bilous1, Charles Cheung2, and Marianna Safronova2 — 1Max Planck Institute for the Science of Light, Staudtstr. 2, 91058 Erlangen, Germany — 2Department of Physics and Astronomy, University of Delaware, Delaware 19716, USA
Atomic structure computations deliver information on atomic properties crucial for applications including atomic frequency standards and analysis of astrophysical spectra. The increasing precision demands lead often to prohibitively large sets of electronic configurations which need to be included in the configuration interaction (CI) framework for accurate modeling of electronic correlations. This necessitates development of efficient configuration selection methods, as well as their integration with existing high-performance atomic codes.
We present a neural-network (NN) approach for efficient selection of electronic configurations integrated with the established pCI atomic codes [1]. The method is applied to otherwise prohibitively large CI computations for the Fe16+ and Ni12+ energy levels and verified within a few cm−1 with an alternative approach of basis upscaling without NN. Our implementation of the NN-supported algorithm allows for integration with other atomic codes providing an efficient and novel tool for a broader atomic physics community.
[1] P. Bilous, C. Cheung, and M. Safronova, Phys. Rev. A 110, 042818 (2024).
Keywords: Atomic structure computations; Electronic correlations; Configuration interaction; Neural networks