LibppRPA: An Open-Source Library for Particle-Particle Random Phase Approximation
Jincheng Yu, Jiachen Li, Chaoqun Zhang, Tianyu Zhu, Weitao Yang

TL;DR
LibppRPA is an open-source Python library that facilitates efficient particle-particle RPA calculations for excited states and correlation energies, integrating seamlessly with existing quantum chemistry software and supporting various computational methods.
Contribution
This work introduces LibppRPA, the first open-source, flexible, and efficient library for particle-particle RPA calculations, enabling broader accessibility and application in quantum chemistry.
Findings
Demonstrates reliable performance across diverse molecular systems.
Supports multiple algorithms for solving RPA equations.
Enables accurate excitation energy and correlation energy calculations.
Abstract
The accurate description of electron correlation and excitation energies remains a fundamental challenge in quantum chemistry. The particle-particle random phase approximation (ppRPA) has emerged as a promising method for capturing a broad range of excited-state properties. However, the implementation of ppRPA has been largely limited to in-house software, restricting its accessibility and usability. In this work, we present LibppRPA, an open-source and lightweight Python library designed for efficient and flexible ppRPA calculations of (1) electronic excitation energy and its associated analytical gradients and (2) the ground state correlation energy, and its associated analytical gradients. LibppRPA enables seamless integration with existing quantum chemistry packages, such as PySCF, by utilizing occupation numbers, molecular orbital coefficients, and three-center electron repulsion…
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Taxonomy
TopicsAdvanced Chemical Physics Studies · Machine Learning in Materials Science · Quantum, superfluid, helium dynamics
