PyGlobal: A toolkit for automated compilation of DFT-based descriptors

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Chemical Reactivity Theory

Background:

• Density Functional Theory (DFT) is crucial for understanding chemical and biological system properties.
• Global reactivity descriptors derived from DFT aid in predicting reactivity, selectivity, and stability.
• Manual extraction of data from computational chemistry outputs can be time-consuming.

Purpose of the Study:

• To develop a Python-based module, PyGlobal, for automated analysis of DFT calculations.
• To streamline the extraction of Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) energies.
• To facilitate the calculation and organization of global reactivity descriptors.

Main Methods:

• Developed a Python module (PyGlobal) to parse Gaussian output files.
• Implemented algorithms to extract HOMO and LUMO energies.
• Integrated calculation of global reactivity descriptors.
• Designed output to be compatible with spreadsheet software (Excel, LibreOffice).

Main Results:

• PyGlobal successfully automates the parsing of Gaussian outfiles.
• The module efficiently calculates global reactivity descriptors.
• Generated data is readily usable in standard spreadsheet applications.
• Efficiency testing demonstrated timely processing of large datasets.

Conclusions:

• PyGlobal offers an efficient and systematic approach to analyzing DFT-derived reactivity descriptors.
• The module enhances the accessibility and usability of computational chemistry data.
• PyGlobal is a valuable tool for researchers studying chemical and biological systems.