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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Main Authors: Kaichen Ma, Chen Yang, Junyao Zhang, Yunfei Li, Gang Jiang, Junjie Chai
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Entropy
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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.
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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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AT junyaozhang machinelearningassistedhartreefockapproachforenergylevelcalculationsintheneutralytterbiumatom
AT yunfeili machinelearningassistedhartreefockapproachforenergylevelcalculationsintheneutralytterbiumatom
AT gangjiang machinelearningassistedhartreefockapproachforenergylevelcalculationsintheneutralytterbiumatom
AT junjiechai machinelearningassistedhartreefockapproachforenergylevelcalculationsintheneutralytterbiumatom