% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Budewig:603200,
      author       = {Budewig, Laura and Son, Sang-Kil and Jurek, Zoltan and
                      Abdullah, Malik Muhammad and Tropmann-Frick, Marina and
                      Santra, Robin},
      title        = {{X}-ray-induced atomic transitions via machine learning:
                      {A} computational investigation},
      journal      = {Physical review research},
      volume       = {6},
      number       = {1},
      issn         = {2643-1564},
      address      = {College Park, MD},
      publisher    = {APS},
      reportid     = {PUBDB-2024-00834},
      pages        = {013265},
      year         = {2024},
      abstract     = {Intense x-ray free-electron laser pulses can induce
                      multiple sequences of one-photon ionization and accompanying
                      decay processes in atoms, producing highly charged atomic
                      ions. Considering individual quantum states during these
                      processes provides more precise information about the x-ray
                      multiphoton ionization dynamics than the common
                      configuration-based approach. However, in such a
                      state-resolved approach, extremely huge-sized rate-equation
                      calculations are inevitable. Here we present a strategy that
                      embeds machine-learning models into a framework for atomic
                      state-resolved ionization dynamics calculations. Machine
                      learning is employed for the required atomic transition
                      parameters, whose calculations possess the computationally
                      most expensive steps. We find for argon that both
                      feedforward neural networks and random forest regressors can
                      predict these parameters with acceptable, but limited
                      accuracy. State-resolved ionization dynamics of argon, in
                      terms of charge-state distributions and electron and photon
                      spectra, are also presented. Comparing fully calculated and
                      machine-learning-based results, we demonstrate that the
                      proposed machine-learning strategy works in principle and
                      that the performance, in terms of charge-state distributions
                      and electron and photon spectra, is good. Our work
                      establishes a first step toward accelerating the calculation
                      of atomic state-resolved ionization dynamics induced by
                      high-intensity x rays.},
      cin          = {CFEL-DESYT / FS-CFEL-3},
      ddc          = {530},
      cid          = {I:(DE-H253)CFEL-DESYT-20160930 /
                      I:(DE-H253)FS-CFEL-3-20120731},
      pnm          = {631 - Matter – Dynamics, Mechanisms and Control
                      (POF4-631) / HIDSS-0002 - DASHH: Data Science in Hamburg -
                      Helmholtz Graduate School for the Structure of Matter
                      $(2019_IVF-HIDSS-0002)$},
      pid          = {G:(DE-HGF)POF4-631 / $G:(DE-HGF)2019_IVF-HIDSS-0002$},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001187539900005},
      doi          = {10.1103/PhysRevResearch.6.013265},
      url          = {https://bib-pubdb1.desy.de/record/603200},
}