BasisOpt: A Python package for quantum chemistry basis set optimization

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Method Development

Background:

• Basis sets are crucial for the accuracy and efficiency of molecular quantum chemical calculations.
• Current basis set development is often hampered by non-public, opaque methodologies and tools.
• Lack of accessibility limits reproducibility and advancement in computational chemistry.

Purpose of the Study:

• To introduce BasisOpt, a novel, user-friendly tool for automated basis set optimization.
• To provide an open and accessible framework for basis set development adaptable to various quantum chemistry programs.
• To standardize basis set testing and visualize optimization processes and results.

Main Methods:

• Development of an automated optimization framework (BasisOpt) with an accessible workflow.
• Implementation of standardized procedures for testing and evaluating optimized basis sets.
• Integration of visualization tools for optimized basis sets and the optimization trajectory.

Main Results:

• Demonstrated successful optimization of a density fitting basis set for noble gases (He, Ne, Ar).
• Showcased re-optimization of def2-SVP basis set exponents for molecular systems, moving beyond atomic optimization.
• Achieved automatic reduction of a large basis set to a compact (10s5p) composition while minimizing energy for noble gases.

Conclusions:

• BasisOpt offers a transparent, adaptable, and efficient solution for basis set optimization in quantum chemistry.
• The tool facilitates the development of novel, high-performance basis sets, advancing computational chemistry.
• Open accessibility and visualization capabilities promote reproducibility and broader adoption of optimized basis sets.