Downloads

Please check out our GitHub repository. New code will be released on GitHub, and most of the downloads found here are on GitHub already.

• Plotting Molecular Orbitals (MOs) with Mathematica: (explore on GitHub)
  
  Rather: Plotting isosurfaces of molecular orbitals... Please follow the link to GitHub shown above, then follow the links that mention orbital plotting, to see detailed descriptions and download options. The notebooks use volume data in the popular cube format.

• Plotting rank-2 tensors: (explore on GitHub)
  
  A Mathematica notebook for plotting graphical representations of NMR shielding tensors; easily adaptable for other types of rank-2 tensors (EFG, Optical Rotation, …).
  Description and some examples
  Download the Mathematica (v. 12 and higher) notebook (60 kByte)
  Here is the notebook for older Mathematica versions (up to v. 11) (52 kByte)
  Download an XYZ molecular coordinate file read by the notebook (16 kByte)
  If you use this plotting tool for your research, please cite the recommended references given at the top in the notebook.

• Crystal field Hamiltonian and atomic shell splitting: (explore on GitHub)
  
  A Mathematica notebook for the symbolic calculation of a crystal field Hamiltonian and the spin-orbit coupling Hamiltonian in a basis of atomic orbitals for a given angular momentum ℓ, along with other calculations.
  Description and some examples
  Downloads the Mathematica notebook (792 kBytes)

• PNMRShift: A software tool for NMR shifts of paramagnetic molecules: (explore on GitHub)
  
  Here you can download the source code along with Linux and Windows (32 bit) binaries of a program that reads calculated magnetic resonance tensors (Ramsey shielding, EPR Zeeman and hyperfine coupling), and optionally zero-field splitting, and assembles chemical shift tensors for a given temperature and pseudo-spin. For details see Reference [224]
  Download PNMRShift (4.2 MByte. GPL)

• KK-GUI: Software with graphical interface to perform Kramers-Kronig transformations: (explore on GitHub)
  This software is useful if you have absorptive or dispersive spectral data and want to perform a Kramers-Kronig (KK) transformation to obtain the dispersive / absorptive counterpart. Works under Linux and Windows and comes in two versions that are both included in the package. Both versions are written in Python and use the Python interface with Tcl/Tk and Matplotlib for the GUI and the resulting plots. One version includes numerical routines in Fortran that need to be compiled. The second version is Python-only and does not require a compiler, but its KK transformations are slower. It is possible to perform ‘anchored’ KK transformations known as multiply subtractive KK (MSKK) or chained doubly-subtractive KK (CDKK); these methods are described in Reference [92]. KK-GUI was developed in 2017 by Mr. Herbert Ludowieg, then an undergraduate research assistant in my group, based on prior developments by Mark Rudolph, Patrick Dawson, and Mikhail Krykunov.
  Download KK-GUI (458 KByte. GPL)
  Below is a screen shot of the interface. We loaded optical rotatory dispersion data (red curve) into the software and let it generate the corresponding circular dichroism spectrum (blue curve).
  

• CD Spectra toolkit:
  Here you can download a package containing some Unix shell scripts and the Fortran source code for two programs. Compiled binaries for a 32 bit Linux system are included. The Fortran source code should compile with any f90 compiler. Please email me if it doesn’t.
  Download gzipped tar archive (781 kByte)
  Together the scripts and programs process the output of a time-dependent DFT CD spectrum calculation and generate a nice simulated spectrum. The CD spectrum can be calculated with ADF or with Turbomole. The parsers are easily adapted for other programs. Please see the included README file for instructions. You need gnuplot to generate the spectra. Here is an example from Reference [17]:
  
  Simulated CD spectrum of [Co(en)3](3+)

© 2013 – 2022 J. Autschbach. Some of the material that can be downloaded on this web page and the associated GitHub repository is in parts or wholly based on the results of research funded by grants from the National Science Foundation [NSF, grants CHE 0447321, 0952253, 1265833, 1560881, 1855470], the US Department of Energy (Basic Energy Sciences, Heavy Element Chemistry program, grant DE-SC0001136), and educational projects supported by these grants. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of these funding agencies.