The Journal of Chemical Physics · 2024 · 20 citations · 73 references
Natural SciencesChemistryPhysicsComputational ChemistryQuantum ChemistryScientific ComputingAb Initio MethodsComputational Quantum ChemistryComputational Quantum MechanicsConfiguration InteractionHartree-fock TheoryAb Initio CalculationsDensity MatricesHigh PerformanceLow-level C ProgrammingQuantum Expectation ValuesReduced Density MatricesSlater DeterminantsSoftware Development Lifecycle
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.
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SciPy 1.0: fundamental algorithms for scientific computing in Python
Nature Methods · 2020 · 37.9K citations · Full text
Coupled-cluster theory in quantum chemistry
Rodney J. Bartlett, Monika Musiał · Reviews of Modern Physics · 2007 · 3.4K citations
Natural Sciences, Chemistry, Physics +14