Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom
Data-driven machine learning approaches with precise predictive capabilities are proposed to address the long-standing challenges in the calculation of complex many-electron atomic systems, including high computational costs and limited accuracy. In this work, we develop a general workflow for machi...
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MDPI AG
2024-11-01
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Online Access: | https://www.mdpi.com/1099-4300/26/11/962 |
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author | Kaichen Ma Chen Yang Junyao Zhang Yunfei Li Gang Jiang Junjie Chai |
author_facet | Kaichen Ma Chen Yang Junyao Zhang Yunfei Li Gang Jiang Junjie Chai |
author_sort | Kaichen Ma |
collection | DOAJ |
description | Data-driven machine learning approaches with precise predictive capabilities are proposed to address the long-standing challenges in the calculation of complex many-electron atomic systems, including high computational costs and limited accuracy. In this work, we develop a general workflow for machine learning-assisted atomic structure calculations based on the Cowan code’s Hartree–Fock with relativistic corrections (HFR) theory. The workflow incorporates enhanced ElasticNet and XGBoost algorithms, refined using entropy weight methodology to optimize performance. This semi-empirical framework is applied to calculate and analyze the excited state energy levels of the 4<i>f</i> closed-shell Yb I atom, providing insights into the applicability of different algorithms under various conditions. The reliability and advantages of this innovative approach are demonstrated through comprehensive comparisons with ab initio calculations, experimental data, and other theoretical results. |
format | Article |
id | doaj-art-8fcd9114d7d24f52b19d93e596acacad |
institution | Kabale University |
issn | 1099-4300 |
language | English |
publishDate | 2024-11-01 |
publisher | MDPI AG |
record_format | Article |
series | Entropy |
spelling | doaj-art-8fcd9114d7d24f52b19d93e596acacad2024-11-26T18:03:13ZengMDPI AGEntropy1099-43002024-11-01261196210.3390/e26110962Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium AtomKaichen Ma0Chen Yang1Junyao Zhang2Yunfei Li3Gang Jiang4Junjie Chai5National Key Laboratory of Particle Transport and Separation Technology, Tianjin 300180, ChinaInstitute of Atomic and Molecular Physics, Sichuan University, Chengdu 610065, ChinaNational Key Laboratory of Particle Transport and Separation Technology, Tianjin 300180, ChinaNational Key Laboratory of Particle Transport and Separation Technology, Tianjin 300180, ChinaInstitute of Atomic and Molecular Physics, Sichuan University, Chengdu 610065, ChinaNational Key Laboratory of Particle Transport and Separation Technology, Tianjin 300180, ChinaData-driven machine learning approaches with precise predictive capabilities are proposed to address the long-standing challenges in the calculation of complex many-electron atomic systems, including high computational costs and limited accuracy. In this work, we develop a general workflow for machine learning-assisted atomic structure calculations based on the Cowan code’s Hartree–Fock with relativistic corrections (HFR) theory. The workflow incorporates enhanced ElasticNet and XGBoost algorithms, refined using entropy weight methodology to optimize performance. This semi-empirical framework is applied to calculate and analyze the excited state energy levels of the 4<i>f</i> closed-shell Yb I atom, providing insights into the applicability of different algorithms under various conditions. The reliability and advantages of this innovative approach are demonstrated through comprehensive comparisons with ab initio calculations, experimental data, and other theoretical results.https://www.mdpi.com/1099-4300/26/11/962atomic calculationmachine learningenergy levelsentropy weight methodytterbium |
spellingShingle | Kaichen Ma Chen Yang Junyao Zhang Yunfei Li Gang Jiang Junjie Chai Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom Entropy atomic calculation machine learning energy levels entropy weight method ytterbium |
title | Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom |
title_full | Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom |
title_fullStr | Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom |
title_full_unstemmed | Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom |
title_short | Machine Learning-Assisted Hartree–Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom |
title_sort | machine learning assisted hartree fock approach for energy level calculations in the neutral ytterbium atom |
topic | atomic calculation machine learning energy levels entropy weight method ytterbium |
url | https://www.mdpi.com/1099-4300/26/11/962 |
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