Machine Learning Predictions of Transition Probabilities in Atomic Spectra

Journal Article · Wed Jan 06 00:00:00 EST 2021 · Atoms

DOI:https://doi.org/10.3390/atoms9010002· OSTI ID:1760448

Michalenko, Joshua J. ^[1]; Murzyn, Christopher M. ^[1]; Zollweg, Joshua D. ^[1]; Wermer, Lydia ^[1]; Van Omen, Alan J. ^[1]; Clemenson, Michael D. ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Forward modeling of optical spectra with absolute radiometric intensities requires knowledge of the individual transition probabilities for every transition in the spectrum. In many cases, these transition probabilities, or Einstein A-coefficients, quickly become practically impossible to obtain through either theoretical or experimental methods. Complicated electronic orbitals with higher order effects will reduce the accuracy of theoretical models. Experimental measurements can be prohibitively expensive and are rarely comprehensive due to physical constraints and sheer volume of required measurements. Due to these limitations, spectral predictions for many element transitions are not attainable. In this work, we investigate the efficacy of using machine learning models, specifically fully connected neural networks (FCNN), to predict Einstein A-coefficients using data from the NIST Atomic Spectra Database. For simple elements where closed form quantum calculations are possible, the data-driven modeling workflow performs well but can still have lower precision than theoretical calculations. For more complicated nuclei, deep learning emerged more comparable to theoretical predictions, such as Hartree–Fock. Unlike experiment or theory, the deep learning approach scales favorably with the number of transitions in a spectrum, especially if the transition probabilities are distributed across a wide range of values. It is also capable of being trained on both theoretical and experimental values simultaneously. In addition, the model performance improves when training on multiple elements prior to testing. The scalability of the machine learning approach makes it a potentially promising technique for estimating transition probabilities in previously inaccessible regions of the spectral and thermal domains on a significantly reduced timeline.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA-20)
• Grant/Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1760448
• Report Number(s): SAND--2021-0130J; 693214
• Journal Information: Atoms, Journal Name: Atoms Journal Issue: 1 Vol. 9; ISSN 2218-2004
• Publisher: MDPI
• Country of Publication: United States
• Language: English

Related Subjects

74 ATOMIC AND MOLECULAR PHYSICS
atomic spectroscopy
deep learning
neural network
transition probability