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Electronic states of the N2+ ion dissociating to the four lowest dissociation limits: Energies, transition dipole moments, and Einstein coefficients.
J Chem Phys
December 2024
Department of Physics, University of Central Florida, Orlando, Florida 32816, USA.
This study presents Born-Oppenheimer energies and transition dipole moments of the 36 lowest electronic states of the N2+ ion as a function of internuclear distance in the interval between 1.5 and 10 bohrs obtained in first-principles calculations. The electronic states are of the total electronic spin S = 1/2, 3/2, and 5/2, dissociating toward to the lowest four N(4S0) + N+(3P), N(2P0) + N+(3P), N(2D0) + N+(3P), and N(4S0) + N+(1D) dissociation limits.
View Article and Find Full Text PDFAuthor Correction: Circular dichroism of relativistically-moving chiral molecules.
Sci Rep
December 2024
Institute of Theoretical Solid State Physics, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany.
Correction to "Anion Vacancies Regulating Endows MoSSe with Fast and Stable Potassium Ion Storage".
ACS Nano
December 2024
Institute for Superconducting & Electronic Materials, School of Mechanical, Materials, Mechatronic, and Biomedical Engineering, University of Wollongong, Wollongong, New South Wales 2522, Australia.
Machine Learning-Assisted Hartree-Fock Approach for Energy Level Calculations in the Neutral Ytterbium Atom.
Entropy (Basel)
November 2024
National Key Laboratory of Particle Transport and Separation Technology, Tianjin 300180, China.
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.
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