An application for data analysis of X-ray absorption fine structure (XAFS). Experimental data of XAFS can be analyzed by various analysis methods. This application supports various analysis functions (high-speed Fourier analysis, fitting in a radial coordinate or k-space, data plotting, etc.) based on IFEFFIT, and includes useful graphical user interface (GUI).
A program package for physical properties related to magnetism. This application can evaluate various physical quantities of magnetics such as crystal fields, magnetic structures, thermodynamic quantities (magnetization, specific heat, etc.), and magnetic excitation. This package can also perform fitting analysis of neutron diffraction experiments and resonant X-ray diffraction experiments, and is helpful to experimentalists.
RESPACK is a first-principles calculation software for evaluating the interaction parameters of materials. It is able to calculate the maximally localized Wannier functions, the RPA response functions, and frequency-dependent electronic interaction parameters. RESPACK receives its input data from a band calculation using norm-conserving pseudopotentials with plane-wave basis sets. Utilities which convert a result of xTAPP or Quantum ESPRESSO to an input for RESPACK are prepared. The software has been used successfully for a wide range of materials such as metals, semiconductors, transition-metal compounds, and organic compounds. It supports OpenMP / MPI parallelization.
TC++ is open-source software for ab initio calculations using the transcorrelated (TC) method. In TC++, users can take account of electron correlations in a Jastrow correlation factor based on the TC method. Electronic structures obtained by Quantum ESPRESSO can be used as an initial state of TC++.
DiracQ is a Mathematica nodebook for calculating commutation relations, which frequently appear in the quantum mechanics. DiracQ can treat canonical operators (canonical momentum and canonical position operators), Fermion operators, and Boson operators.
An open-source application for simulation of one-dimensional interacting electron models based on a tensor product wavefunction method. This application supports not only electronic models but also spin and bosonic models, and can evaluate various physical quantities for ground states and low-lying excited states. This application also supports time evolution, and can treat models with long-range interactions.
An exact diagonalization package for efficiently solving quantum spin 1/2 lattice models in almost fully spin-polarized sectors. QS3 can treat such systems with quite large system sizes, over 1000 sites. It supports calculations of wavenumber-dependence of energy-dispersion and dynamical spin structure factor.
An open-source application for simulation based on the density-matrix renormalization group (DMRG). This application can perform high-speed calculation of low-dimensional quantum systems with high accuracy. It implements generic programming techniques in the C++ language, and can easily extend simulation to new models and geometries. It is developed putting emphasis on user-friendly interfaces and low dependences on environments.
An exact diagonalization package for a wide range of quantum lattice models (e.g. multi-orbital Hubbard model, Heisenberg model, Kondo lattice model). HΦ also supports the massively parallel computations. The Lanczos algorithm for obtaining the ground state and thermal pure quantum state method for finite-temperature calculations are implemented. In addition, dynamical Green’s functions can be calculated using Kω, which is a library of the shifted Krylov subspace method. It is possible to perform simulations for real-time evolution from ver. 3.0.
NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques. Users can perform machine learning algorithms to find the ground-state of many-body Hamiltonians such as supervised learning of a given state and optimization of neural network states by using the variational Monte Carlo method.