A neural-network-based Python package for performing large-scale atomic CI using pCI and other high-performance atomic codes

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.

Program summary

Program Title: nn_for_pci

CPC Library link to program files: https://doi.org/10.17632/yy29nhwkbw.1

Developer's repository link: https://github.com/pavlobilous/nn_for_pci

Licensing provisions: GPLv3

Programming language: Python

Nature of problem: Exponential scaling of the basis size in the atomic CI approach

Solution method: Iterative NN-based selection of the relevant basis elements out of a large CI basis

References

• [1]
  P. Bilous, C. Cheung, M. Safronova, Phys. Rev. A 110 (2024) 042818.
• [2]
  C. Cheung, M.G. Kozlov, S.G. Porsev, M.S. Safronova, I.I. Tupitsyn, A.I. Bondarev, Comput. Phys. Commun. 308 (2025) 109463.