PyCC: A Reference Quantum Chemistry Python Package for Education, Validation, and Rapid Prototyping
T. Daniel Crawford, Benjamin G. Peyton, Zhe Wang, Aparna Krishnan, Brendan M. Shumberger, Jose Madriaga, Joshua Atta-Kumi, Aaron De Clercq, Ritvik Prabhu
physics.chem-phquant-phSubmitted Oct 2, 2026• Announced Oct 6, 2026
PyCC is a Python-based quantum chemistry package that emphasizes coupled cluster and related methods. Developed over the last five years, PyCC's purpose is to provide a clear reference implementation that will serve as a validation suite for production code, a platform for rapid prototyping and testing of new methods, and an educational resource for newcomers to the field. The package covers both closed- and open-shell single-determinant references in spatial- and spin-orbital formulations for the coupled cluster hierarchy through triples-including methods such as CC3, as well as for MP2 and CI methods. PyCC provides a broad range of molecular properties such as analytic first and second energy derivatives including gradients, Hessians, polarizabilities and atomic polar tensors, as well as non-energy-derivative properties, such as velocity-gauge atomic polar tensors and diagonal Born-Oppenheimer corrections, plus dynamic linear response functions, and excitation energies. The package includes a number of unique capabilities, such as real-time CC propagation (up through CC3), atomic axial tensors for simulating vibrational circular dichroism spectra at the MP2 and CI levels, and local correlation methods including perturbation-aware correlation domains. The code is designed around a shared Wavefunction base and a method-agnostic derivative layer that enables a new method to inherit the full property set once it supplies its reduced densities and their first-order responses. PyCC is fully open source and available on GitHub under the BSD 3-clause license.
PyCC is a Python-based quantum chemistry package that emphasizes coupled cluster and related methods. Developed over the last five years, PyCC's purpose is to provide a clear reference implementation that will serve as a validation suite for production code, a platform for rapid prototyping and testing of new methods, and an educational resource for newcomers to the field. The package covers both closed- and open-shell single-determinant references in spatial- and spin-orbital formulations for the coupled cluster hierarchy through triples-including methods such as CC3, as well as for MP2 and CI methods. PyCC provides a broad range of molecular properties such as analytic first and second energy derivatives including gradients, Hessians, polarizabilities and atomic polar tensors, as well as non-energy-derivative properties, such as velocity-gauge atomic polar tensors and diagonal Born-Oppenheimer corrections, plus dynamic linear response functions, and excitation energies. The package includes a number of unique capabilities, such as real-time CC propagation (up through CC3), atomic axial tensors for simulating vibrational circular dichroism spectra at the MP2 and CI levels, and local correlation methods including perturbation-aware correlation domains. The code is designed around a shared Wavefunction base and a method-agnostic derivative layer that enables a new method to inherit the full property set once it supplies its reduced densities and their first-order responses. PyCC is fully open source and available on GitHub under the BSD 3-clause license.
Psi4NumPy demonstrates the use of efficient computational kernels from the open-source Psi4 program through the popular NumPy library for linear algebra in Python to facilitate the rapid development of clear, understandable Python computer code for new quantum chemical methods, while maintaining a relatively low execution time. Using these tools, reference implementations have been created for a number of methods, including self-consistent field (SCF), SCF response, many-body perturbation theory, coupled-cluster theory, configuration interaction, and symmetry-adapted perturbation theory. Furthermore, several reference codes have been integrated into Jupyter notebooks, allowing background, underlying theory, and formula information to be associated with the implementation. Psi4NumPy tools and associated reference implementations can lower the barrier for future development of quantum chemistry methods. These implementations also demonstrate the power of the hybrid C++/Python programming approach employed by the Psi4 program.
macroqc is a quantum chemistry software package designed for high-accuracy electronic structure computations and large-scale molecular simulations. The macroqc framework provides efficient implementations of energies and analytic gradients for a wide range of many-body perturbation theory and coupled-cluster (CC) methods. In particular, the analytic gradient implementations of second-order Møller-Plesset perturbation theory (MP2), coupled-cluster singles and doubles (CCSD), and CCSD with perturbative triples [CCSD(T)] exhibit excellent computational performance, even in comparison with established commercial quantum chemistry programs. In addition to conventional correlated methods, macroqc incorporates several advanced capabilities, including molecular fragmentation techniques for ground and excited state molecular systems and density-fitted analytic gradients. The software also supports interoperability with selected external packages through a flexible plugin framework, allowing developers to construct customized interfaces and extensions. The notable features of macroqc are the linear-scaling systematic molecular fragmentation (LSSMF) and the cluster-in-molecule (CIM) approaches, which enable the application of high-level post-Hartree-Fock approaches to large molecular systems that would otherwise be computationally prohibitive. Furthermore, the new machine learning (ML) library enables the integration of data-driven approaches into quantum chemical workflows, providing efficient tools for developing and applying ML-based models for molecular property prediction and electronic structure computations. With its combination of efficient implementations, advanced theoretical methods, and extensible design, macroqc is expected to serve as a useful platform for a broad range of computational chemistry applications.
Automated Quantum Chemistry Code Generation with the p†q Package
This article summarizes recent updates to the p†q package, which is a C++ accelerated Python library for generating equations and computer code corresponding to singly-reference many-body quantum chemistry methods such as coupled-cluster (CC) and equation-of-motion (EOM) CC theory. Since 2021, the functionality in \pq has expanded to include boson operators, coupled fermion-boson operators, unitary cluster operators, non-particle-conserving EOM operators, spin tracing, multiple single-particle subspaces, and more. Additional developments allow for the generation of C++ and Python code that minimizes floating-point operations via contraction order optimization, sub-expression elimination, and the fusion of similar terms.
PyCI is a free and open-source Python library for setting up and running arbitrary determinant-driven configuration interaction (CI) computations, as well as their generalizations to cases where the coefficients of the determinant are nonlinear functions of optimizable parameters. PyCI also includes functionality for computing the residual correlation energy, along with the ability to compute spin-polarized one- and two-electron (transition) reduced density matrices. PyCI was originally intended to replace the ab initio quantum chemistry functionality in the HORTON library but emerged as a standalone research tool, primarily intended to aid in method development, while maintaining high performance so that it is suitable for practical calculations. To this end, PyCI is written in Python, adopting principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. Computationally intensive steps, notably operations related to generating Slater determinants and computing their expectation values, are delegated to low-level C++ code. This article marks the official release of the PyCI library, showcasing its functionality and scope.
This article summarizes recent updates to the p † q package, which is a C++ accelerated Python library for generating equations and computer code corresponding to singly referenced many-body quantum chemistry methods such as coupled-cluster (CC) and equation-of-motion (EOM) CC theory. Since 2021, the functionality in p † q has expanded to include boson operators, coupled fermion-boson operators, unitary cluster operators, non-particle-conserving EOM operators, spin tracing, multiple single-particle subspaces, and more. Additional developments allow for the generation of C++ and Python code that minimizes floating-point operations via contraction order optimization, subexpression elimination, and the fusion of similar terms.
DAMPyF is an open-source Python implementation of the dissipation-assisted matrix product factorization (DAMPF) method, a tensor-network-based approach for the numerically exact simulation of finite-dimensional quantum systems coupled to bosonic environments. The method relies on a pseudomode representation of structured reservoirs and a matrix-product-state representation of the density matrix of the extended system, comprising the system and the pseudomodes. DAMPyF currently provides two workflows. First, it supports excitation energy-transfer dynamics within the single-system-excitation manifold, in which a system excitation is propagated in time. Second, it provides a high-level workflow tailored to molecular spectroscopy, in which the system levels represent electronic states and optical coherences are propagated for the subsequent computation of linear spectra, including absorption and circular dichroism. This paper describes the physical model, the DAMPF algorithm, the user-facing code structure, installation and execution, input and output formats, and minimal examples.