Category: Strong correlation/effective models

Integrated package for model and ab initio calculations with dynamical mean-field theory (DMFT)

• Obtain spectral functions of strongly correlated electron systems with DMFT
• Run DMFT calculations combined with first-principles calculations

Path-integral quantum Monte Carlo solver for quantum lattice systems at finite temperature

• Compute finite-temperature properties of quantum spin systems and Bose-Hubbard models

Python package that analyzes itinerant electron systems with the unrestricted Hartree-Fock (UHF) approximation and the random phase approximation (RPA)

• Study ordered phases with the unrestricted Hartree-Fock approximation
• Compute susceptibilities with the random phase approximation (RPA)

Large-scale exact diagonalization solver for quantum lattice models

• Compute ground states of spin and itinerant-electron models
• Obtain dynamical correlation functions (spectra)

Solver for strongly correlated electron systems based on the many-variable variational Monte Carlo method

• Obtain ground states of strongly correlated electron models variationally
• Compute correlation functions of large systems

Massively parallel tensor-network solver for two-dimensional quantum lattice models

• Study ground-state and finite-temperature properties of 2D quantum spin and boson systems
• Run tensor-network (iTPS) calculations at scale in parallel

Solves the Bethe-Salpeter equation in DMFT to compute momentum-dependent two-particle susceptibilities (post-processing for DCore)

• Obtain dynamical and static susceptibilities from DCore results

Database of intermediate-representation (IR) basis functions for imaginary-time Green's functions, with Python and C++ libraries

• Expand imaginary-time Green's functions in the IR basis

Sparse-modeling tool for analytic continuation of imaginary-time Green's functions

• Obtain real-frequency spectral functions from imaginary-time Green's functions computed by quantum Monte Carlo

Generates input files for HPhi, mVMC, H-wave and others from a simple configuration file

• Quickly prepare input files for standard lattice models

Python library for constructing and working with the intermediate representation (IR) of propagators

• Compute IR basis functions for arbitrary cutoff Λ in Python
• Perform many-body calculations such as GW or FLEX using sparse sampling

Julia library for constructing and working with the intermediate representation (IR) of correlation functions

• Compute IR basis functions for arbitrary cutoff Λ in Julia
• Handle Matsubara Green's functions efficiently with sparse sampling

Tool for analytic continuation of imaginary-time Green's functions by sparse modeling

• Obtain spectral functions from imaginary-time quantum Monte Carlo data
• Analytically continue multi-orbital Green's functions imposing semi-positive definiteness

Julia implementation of Nevanlinna analytic continuation

• Obtain real-frequency spectral functions from Matsubara Green's functions

Tool that combines HΦ and PHYSBO to determine quantum lattice model parameters from magnetization curves by Bayesian optimization

• Find Hamiltonian parameters that reproduce a target magnetization curve

C++ library for the intermediate representation (IR) of correlation functions and sparse sampling (archived; succeeded by sparse-ir-rs)

• Compute IR basis functions for an arbitrary cutoff Λ to full precision

Rust implementation of the intermediate representation (IR), the discrete Lehmann representation (DLR) and sparse sampling for imaginary-time Green's functions

• Represent imaginary-time Green's functions compactly with few coefficients
• Speed up many-body calculations with sparse sampling

Sparse-modeling analytic continuation

• Perform analytic continuation with sparse modeling

Fortran interface for sparse-ir, the intermediate representation (IR) basis and sparse sampling library

• Use the IR basis and sparse sampling in Fortran codes

Developer library for computing intermediate representation (IR) basis functions

• Generate intermediate representation (IR) basis functions with high precision

Linear-equation solver library based on the shifted Krylov subspace method (dynamical Green's functions)

• Compute dynamical Green's functions at many frequencies efficiently

Julia wrapper for libsparseir, a C++ library for the sparse intermediate representation (IR) of correlation functions (under development; SparseIR.jl is recommended for production use)

• Perform sparse sampling in imaginary time and Matsubara frequencies by calling libsparseir from Julia

Python bindings for libsparseir, a sparse intermediate representation (IR) library

• Handle fermionic and bosonic Green's functions with the sparse IR method from Python

[Tests] Test project for using libsparseir (sparse intermediate representation (IR) library) from Fortran

[Examples] HPhi input/output files with descriptions of the models

[Tutorial] Learning material for using mVMC

[Documentation] Guide to the sparse-ir ecosystem: basics of the intermediate representation (IR), Green's functions, DLR, Matsubara sums, and shared notation and conventions

[Environment] Docker image including the sparse-ir libraries and their tutorials

[Tutorial] Tutorials for the sparse-ir libraries v1 (deprecated and no longer maintained; see the v2 tutorial)

[Tutorial] Current tutorials for sparse-ir (Python) and SparseIR.jl (Julia) v2: notebooks on DMFT, FLEX, GW, TPSC, the Eliashberg equation, analytic continuation and more

[Tutorial] Sample notebooks for sparse sampling using irbasis

[Examples] Calculation examples for Komega

[Environment] Docker environment for running the sparse-ir tutorial on Binder