LibppRPA: An open-source library for particle-particle random phase approximation

Area of Science:

• Quantum Chemistry
• Computational Chemistry
• Electronic Structure Theory

Background:

• Accurate calculation of electron correlation and excitation energies is a key challenge.
• Particle-particle random phase approximation (ppRPA) shows promise for excited-state properties.
• Existing ppRPA implementations are often restricted to in-house software, limiting accessibility.

Purpose of the Study:

• Introduce LibppRPA, an open-source Python library for efficient and flexible ppRPA calculations.
• Enable calculation of electronic excitation energies and ground state correlation energies with analytical gradients.
• Facilitate integration with existing quantum chemistry packages.

Main Methods:

• Developed a lightweight Python library, LibppRPA.
• Implemented direct diagonalization and iterative Davidson algorithms for solving ppRPA equations.
• Incorporated active-space approximations to balance accuracy and computational cost.

Main Results:

• Demonstrated LibppRPA's performance through benchmark calculations.
• Showcased reliability across various excitation types (singlet-triplet gaps, double, charge-transfer, valence/Rydberg excitations).
• Validated the library's capability for diverse molecular systems.

Conclusions:

• LibppRPA offers a robust and accessible platform for studying electronic excitations.
• The library enables seamless integration with packages like PySCF.
• LibppRPA opens avenues for future advancements in electronic structure theory.