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From: Atomiccalculator <atomiccalculator-bounces@listserv.uni-jena.de> on behalf of Stephan Fritzsche <stephan.fritzsche@uni-jena.de>
Sent: Monday, November 4, 2019 9:54 AM
To: atomiccalculator@listserv.uni-jena.de
Subject: [Atomiccalculator] Jena Atomic Calculator (JAC code) for the computation of atomic structures, processes, cascades & others.

 

                                                                       
       November 2019
Dear Colleagues and Friends,

this is a very first (public) announcement of JAC, the Jena Atomic 
Calculator. JAC is an
open-source Julia package [cf. https://julialang.org/] and a platform 
for (just) atomic
computations. In particular, JAC is a (relativistic atomic) code which 
help perform atomic
computations at various degrees of complexity and sophistication. JAC 
has been originally
developed for computations of interaction amplitudes, properties as 
well as a large number
of excitation and decay processes for open-shell atoms and ions across 
the periodic table.
Please, see CPC 240 (2019) 1 
[https://doi.org/10.1016/j.cpc.2019.01.012] for a brief
overview of the set-up of the JAC code.

During recent months, moreover, JAC has been extended towards atomic 
representations & cascades,
atomic descriptors for machine learning as well as towards symbolic 
simplifications of
expressions from Racah's algebra. This makes JAC a powerful tool for 
atomic physics simulations.

You can simply download and install JAC from the website: 
https://github.com/OpenJAC/JAC.jl
A primary guiding philosophy of JAC was to develop a general and 
easy-to-use toolbox for the
atomic physics community, including an interface that is equally 
accessible for working
spectroscopists, theorists, researchers in atomic, astro and plasma 
physics as well as
for code developers. The package comes along also with a detailed User 
Guide & Compendium
[https://github.com/OpenJAC/JAC.jl/blob/master/UserGuide-Jac.pdf] that 
describes the use
and underlying atomic theory of the JAC code.


Among several other features, JAC currently supports (or will do so 
hopefully soon) computations
the following kind:

+ Electronic structure calculations for open-shell atoms and ions 
across the periodic table
     (these computations are presently based on a local central-field 
potential but may account
      for QED estimates & model potentials, jjJ-LSJ transformation of 
atomic levels, Green
      function approximations, ...)
+ Computation of properties and processes, based on explicitly 
specified electron configurations
     (e.g. transition probabilities, hyperfine & isotope-shift 
parameters, atomic form factors,
      plasma shifts, decay yields, photoionization & recombination, 
Auger emission, dielectronic
      recombination, Rayleigh scattering, multi-photon processes, 
Coulomb excitation of ions, ...)
+ Representation of atomic wave funtions, including restricted 
active-space (RAS) computations
     (by making use of systematically enlarged many-electron basis; 
configuration interaction,
      complex scaling, ...)
+ Interactive computations
     (by making use of JAC's high-level language to describe atomic 
structures and processes)
+ Atomic cascade computations
     (e.g. ion yield spectra following inner-shell excitations, 
electron & photon spectra,
      generation of cascade trees, ...)
+ Atomic responses
     (e.g. generation of high-harmonic spectra & profiles, ...)
+ Calculation of atomic descriptors for machine learning
+ Atomic time-evolution of statistical tensors
+ Semi-empirical estimates of selected atomic properties
+ Symbolic evaluation of expressions from Racah's algebra
     (simplification of (Racah) expressions with any number of Wigner 
n-j symbols, spherical
      harmonics, Wigner rotation matrices, etc; base on a large set of 
special values and
      sum rules)


While Julia always compiles just-in-time all executed code, JAC 
seemingly provides the user
with an easy-to-learn, interactive, high-level language in order to 
support and facilitate the
set-up of most typical atomic computations and simulations. Moreover, 
a number of IJulia/jupyter
notebooks demonstrate different features of JAC and may quickly help 
the user to understand how
such computations can be easily controlled and carried out with very 
moderate effort.

Although JAC is still in an early stage of development (including 
various features that are
only partly implemented or not yet tested in proper detail), I here 
wish to annouce JAC and kindly
ask you as a potential user and developer for response, support and 
encouragement for this
open-source project. You are welcome to join this project to request 
for additional features to
this code.


Please, feel free to distribute this email to those who might be 
interested in this code.
Thank you in advance for your support and with best wishes,
     Sincerely yours
         Stephan Fritzsche