KNIME workflows and NBO analysis
KNIME is data analytics and visualization environment that is quite intuitive to use. There is no prior scripting knowledge required, although skills in Java programming will become handy. Most data manipulation and analysis is accomplished by dragging and dropping in different functions or processes, the so called “nodes”. The KNIME platform has a variety of libraries and add-on features (extensions) that increase its functionality. Those include R statistical package, MATLAB, Python, Perl, and a wide variety of extensions for chemistry. KNIME is open source software, freely available as executable file for Windows, MAC OS, and Linux.
To facilitate analysis and chemical interpretation of NBO results, I chose the Knime software and built several workflows around the GENNBO5/6 output files.
2nd Order Perturbation Theory (E2pert) - Results and Visualization Workflow
This is the first KNIME workflow, which extracts outputs of the 2nd order perturbation theory from the .nbo file. Processed data are written into a java table and the corresponding .pdf and .csv files are saved on local disk. In order to provide visual representation of interacting donor-acceptor NBO pairs, KNIME's output is directed to R script. Components of the KNIME-R workflow are discussed in details in the accompanying blog. KNIME archive with input files is available for download.
Multiple KNIME Workflows - Results and Visualization of NBO Properties
Multi properties Workflow
This KNIME workflow expands on the previous E2Pert workflow by adding four more streams, namely NBO Summary, Dipole Moment, Steric Analysis, and NBO Charges. Processed data are written into a Java table and the corresponding .pdf and .csv files are saved on local disk. In order to provide visual representation of different properties, KNIME's output is directed to R scripts. As an input, either GENNBO .nbo or Gaussian .out files can be used. Examples and the complete workflow can be downloaded below.
Knime-Python-Anaconda and Pandas - Processing and Visualization of Natural Dipoles
Knime-Python-Pandas and NBO dipoles
To facilitate analysis and visualization of natural dipole moments obtained from the Natural Bond Orbital (NBO) analysis of molecular wavefunction, KNIME workflow implementing Python script nodes is described. Python libraries such as NumPy and Pandas are used to demonstrate the benefits of Python implementation.
Two workflows are available for download. Dipoles_v2.zip was created under KNIME 2.9 while Dipoles_v3.zip workflow includes improvements in Python and R-nodes released in the KNIME Analytical Platform 2.11.