ORBKIT

Official Resources

• Homepage: https://orbkit.github.io/
• GitHub: https://github.com/orbkit/orbkit
• Documentation: https://orbkit.github.io/
• Publication: G. Hermann et al., J. Comput. Chem. 37, 1511 (2016)
• License: GNU Lesser General Public License v3.0

Overview

ORBKIT is a parallel Python program package for post-processing wavefunction data from quantum chemical programs. It computes grid-based quantities (molecular orbitals, electron density, electrostatic potential) and non-grid-based quantities (Mulliken charges, bond orders) from various quantum chemistry output formats.

Scientific domain: Wavefunction analysis, electron density, molecular orbitals Target user community: Quantum chemists analyzing molecular electronic structure

Theoretical Methods

• Molecular orbital visualization
• Electron density computation
• Electrostatic potential mapping
• Mulliken population analysis
• Bond order calculation
• Transition density analysis

Capabilities (CRITICAL)

• Molecular Orbitals: Grid-based visualization
• Electron Density: Total and spin density
• Population Analysis: Mulliken charges
• Bond Orders: Mayer and other indices
• Multi-Code Support: Gaussian, ORCA, MOLPRO, GAMESS, etc.
• Parallel Processing: MPI and multiprocessing
• Cube Files: Standard visualization output

Sources: ORBKIT documentation, JCC publication

Key Strengths

Multi-Code Support:

• Gaussian, ORCA, MOLPRO
• GAMESS, Turbomole, PSI4
• Molden format support
• Consistent interface

Parallel Processing:

• MPI parallelization
• Multiprocessing support
• Efficient grid evaluation
• Large system capable

Python Native:

• NumPy/SciPy based
• Scriptable workflows
• Jupyter compatible
• Easy installation

Inputs & Outputs

• Input formats:

  • Gaussian log/fchk files
  • ORCA output
  • Molden format
  • MOLPRO, GAMESS, Turbomole

• Output data types:

  • Cube files
  • HDF5 data
  • Mulliken populations
  • Bond orders

Installation

pip install orbkit
# Or from source
git clone https://github.com/orbkit/orbkit.git
cd orbkit
pip install -e .

Usage Examples

from orbkit import read, grid, core

# Read wavefunction
qc = read.main_read('molecule.molden')

# Set up grid
grid.adjust_to_geo(qc, extend=5.0)
grid.grid_init()

# Compute electron density
rho = core.rho_compute(qc)

# Save as cube file
from orbkit import output
output.cube_creator(rho, 'density.cube', qc)

Performance Characteristics

• Speed: Parallel grid evaluation
• Memory: Efficient for large grids
• Scalability: MPI for clusters

Limitations & Known Constraints

• Molecular focus: Primarily for molecules
• Periodic systems: Limited support
• File formats: Some codes not supported
• Documentation: Could be more extensive

Comparison with Other Tools

• vs Multiwfn: ORBKIT Python-native, Multiwfn GUI
• vs ChemTools: Different analysis focus
• vs Critic2: ORBKIT molecules, Critic2 periodic
• Unique strength: Python ecosystem, parallel processing

Application Areas

• Molecular orbital visualization
• Charge distribution analysis
• Reaction mechanism studies
• Excited state analysis
• Transition density mapping

Best Practices

• Verify wavefunction file format
• Use appropriate grid resolution
• Validate against known systems
• Leverage parallel processing

Community and Support

• GitHub repository
• JCC publication
• Active development
• LGPL licensed

Verification & Sources

Primary sources:

1. GitHub: https://github.com/orbkit/orbkit
2. G. Hermann et al., J. Comput. Chem. 37, 1511 (2016)
3. Documentation: https://orbkit.github.io/

Confidence: VERIFIED - Published in JCC

Verification status: ✅ VERIFIED

• GitHub repository: ACCESSIBLE
• Documentation: COMPREHENSIVE
• Source code: OPEN (LGPL-3.0)
• Academic citations: Well-cited
• Active development: Maintained