A neural-network-based Python package for performing large-scale atomic CI using pCI and other high-performance atomic codes
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| Format: | Preprint |
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2025
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| _version_ | 1866910854732054528 |
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| author | Bilous, Pavlo Cheung, Charles Safronova, Marianna |
| author_facet | Bilous, Pavlo Cheung, Charles Safronova, Marianna |
| contents | 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). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_01379 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A neural-network-based Python package for performing large-scale atomic CI using pCI and other high-performance atomic codes Bilous, Pavlo Cheung, Charles Safronova, Marianna Atomic Physics 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). |
| title | A neural-network-based Python package for performing large-scale atomic CI using pCI and other high-performance atomic codes |
| topic | Atomic Physics |
| url | https://arxiv.org/abs/2503.01379 |