2025/03/03 by Pavlo Bilous, Bilous, Pavlo, Charles Cheung +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Atomic Physics (physics.atom-ph) #Atomic and Subatomic Physics Research #FOS: Physical sciences #Fractal and DNA sequence analysis
paper · pdf · doi:10.48550/arxiv.2503.01379
openalex publication_date 2025/03/03 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28
Modern atomic physics applications in science and technology pose ever higher demands on the precision of computations of properties of atoms and ions. Especially challenging is the modeling of electronic correlations within the configuration interaction (CI) framework, which often requires expansions of the atomic state in huge bases of Slater determinants or configuration state functions. This can easily render the problem intractable even for highly efficient atomic codes running on distributed supercomputer systems. Recently, we have successfully addressed this problem using a neural-network (NN) approach [1]. In this work, we present our Python code for performing NN-supported large-scale atomic CI using pCI [2] and other high-performance atomic codes. [1] P. Bilous, C. Cheung, and M. Safronova, Phys. Rev. A 110 042818 (2024). [2] C. Cheung, M. G. Kozlov, S. G. Porsev, M. S. Safronova, I. I. Tupitsyn, A. I. Bondarev, Comput. Phys. Commun. 308 109463 (2025).