Bulletin of the American Physical Society
76th Annual Gaseous Electronics Conference
Volume 68, Number 9
Monday–Friday, October 9–13, 2023; Michigan League, Ann Arbor, Michigan
Session IT4: Poster Session I; Exhibition & Coffee (4:00pm-6:00pm)
4:00 PM,
Tuesday, October 10, 2023
Room: Michigan League, Ballroom
Abstract: IT4.00001 : Bayesian optimization in atomic structure calculations for collisional problems
Presenter:
Alejandra M Mendez
(Instituto de Astronomía y Física del Espacio)
Authors:
Alejandra M Mendez
(Instituto de Astronomía y Física del Espacio)
Darío M Mitnik
(Instituto de Astronomía y Física del Espacio)
In this contribution, we implemented the Bayesian method via Gaussian processes (GP) to optimize the atomic structure of the ions. This optimization is an excellent machine learning methodology to minimize scalar-valued error functions. To illustrate the procedure, we considered the neutral beryllium atom. An accurate description of the target has proven to be necessary for obtaining accurate electron impact excitation and electron impact ionization cross-sections [1, 2].
The atomic structure of Be and various transition metals is calculated with the autostructure code [3]. The scaling parameters of the model potentials used define the parameters of the atomic structure calculation. These parameters are then optimized within the Bayesian approach. The energy and oscillator strengths values of the most low-lying terms obtained generally agree with experimental values within 1% and 10%, respectively.
[1] Zatsarinny O et al 2016 J. Phys. B 49 235701
[2] Ballance C P et al 2003 Phys. Rev. A 68 062705
[3] Badnell N R 2011 Comput. Phys. Commun. 7 1528
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