libmsym
libmsym performs point group symmetry analysis and symmetrization of molecular geometries and quantum wavefunctions to support computational quantum chemistry calculations.
Key Features:
- Automatic Detection of Point Groups: Employs a clustering algorithm to identify symmetry-invariant properties and uses logical deduction to determine possible symmetry elements from the geometric arrangement of symmetrically equivalent atoms.
- Symmetrisation Algorithms: Projects molecular geometries into the totally symmetric subspace and projects wavefunctions while determining partner functions and averaging them.
- Symmetry Adapted Linear Combinations (SALCs): Determines SALCs of atomic orbitals using projection operators associated with irreducible representations and subgroups that define splitting fields for a canonical basis.
- Auto-Generated Character Tables: Automatically generates character tables for point groups.
Scientific Applications:
- Quantum Chemical Calculations: Ensures wavefunctions and geometries respect molecular symmetries to improve the accuracy of electronic structure calculations.
- Molecular Modelling Software Integration: Provides symmetry analysis, SALC construction, and character tables for incorporation into computational and molecular modeling platforms.
Methodology:
Uses a clustering algorithm to identify symmetry-invariant properties and logical deduction to infer symmetry elements; applies projection operators for SALCs and subgroup theory to define splitting fields for a canonical basis; projects geometries into the totally symmetric subspace, projects wavefunctions, determines partner functions, averages them, and auto-generates character tables for point groups.
Topics
Details
- License:
- MIT
- Tool Type:
- library
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- Python, C
- Added:
- 9/1/2018
- Last Updated:
- 11/25/2024
Operations
Publications
Johansson M, Veryazov V. Automatic procedure for generating symmetry adapted wavefunctions. Journal of Cheminformatics. 2017;9(1). doi:10.1186/s13321-017-0193-3. PMID:28217147. PMCID:PMC5289936.