GBasis: A Python library for evaluating functions, functionals, and integrals expressed with Gaussian basis functions

Area of Science:

• Quantum Chemistry
• Computational Chemistry
• Molecular Modeling

Background:

• Gaussian basis functions are fundamental in quantum chemistry for representing atomic and molecular orbitals.
• Efficient computation of molecular properties and integrals is crucial for advancing theoretical chemistry.
• Existing tools may lack flexibility or comprehensive features for advanced calculations.

Purpose of the Study:

• To introduce GBasis, a free and open-source Python library for molecular property computations.
• To provide a flexible and extensible tool for evaluating functions and integrals based on Gaussian basis sets.
• To facilitate high-performance computations through an interface with the Libcint C package.

Main Methods:

• Development of a Python library utilizing Gaussian basis functions.
• Implementation of functions for evaluating molecular orbitals, electron density, and density matrices.
• Computation of overlap, one-electron, and two-electron integrals.
• Inclusion of arbitrary-order derivative calculations and various Coulomb interactions.
• Provision of a Python interface to the high-performance Libcint C package.

Main Results:

• GBasis enables evaluation of arbitrary-order derivatives of density matrices.
• The library supports a wide range of screened Coulomb interactions.
• GBasis facilitates the evaluation of overlap integrals for numerous Gaussians in high dimensions.
• A seamless Python interface to Libcint ensures high performance when needed.

Conclusions:

• GBasis is a versatile and user-friendly Python library for quantum chemistry.
• It offers advanced features for molecular property computations and integral evaluations.
• The library adheres to sustainable software development principles, ensuring maintainability and extensibility.