An application for evaluation of thermoelectric properties and its visualization. Seebeck coefficients and Peltier coefficients can be calculated from output of the first-principles applications, OpenMX and TranSIESTA. Obtained results as well as electron density and density of states can be visualized.
An open-source application for quantum chemical calculation. This package implements various methods for quantum chemical calculation such as Hartree-Fock approximation, density functional theory, coupled-cluster method, and CI (configuration interaction) method. The package is written in C++, and provides API for Python, by which users can perform for preparation of setting and execution of calculation.
BEEMs is a Bayesian optimization tool of Effective Models (BEEMs). In BEEMs, the quantum lattice model solver HΦ is used as a forward problem solver to compute the magnetisation curve based on the given Hamiltonian. The deviation between the obtained magnetisation curve and the target magnetisation curve is used as a cost function, and the Bayesian optimization library PHYSBO is used to propose the next candidate point of the Hamiltonian for searching the minimum cost function
An application for quantum chemical calculation based on DFTB (Density Functional based Tight Binding). This application performs structure
optimization and molecular dynamics by the DFTB force field as well as ordinary energy calculation, and implements parallel computing by OpenMP. A tool for visualization of molecular orbitals and an extended versions supporting MPI parallel computation or electron transport calculation by the nonequilibrium Green’s function method are also
An open-source application for molecular modeling and visualization. This application supports data formats of Gaussian, GAMESS, ADF, and Molden, and has various options for drawing such as orbital, electron density, solvent accessible surface, van der Waals radii, and so on. It implements high-speed and high-quality rendering technology, and runs on Windows, Mac, and Linux.
An open-source application for high-accuracy electronic-state calculation based on the variational Monte Carlo method and the diffusion Monte Carlo method. Although its computational cost is high, physical properties of atoms and small molecules in the ground states and excited states are calculated with very high accuracy. Includes an application program that generates input files from output of other packages for quantum chemical calculation, such as GAMESS, Gaussian, etc.
An open-source impurity solver based on the quantum Monte Carlo method. Thermal equilibrium states of interacting impurity systems, such as the impurity Anderson model, can be evaluated by the continuous-time hybridization-expansion quantum Monte Carlo method. It can be used as a solver of effective impurity models derived from the dynamical mean-field theory (DMFT) and can deal with multi-orbital models. This package supports parallel computation by MPI and is developed based on the ALPSCore library.
A open-source application of first-principles calculation for the electronic structure, using the KKR method, a variant of Green’s function method. It is based on the density functional theory and is applicable to crystals and surfaces. The coherent potential approximation (CPA) is adopted, so it can handle not only periodic systems, but also disordered alloys. It can also handle spin-orbit interaction and non-collinear magnetism.
An application for visualization of large-scale many-particle simulation. This application can visualize information on a large number of particles treated in calculation of gravitational many-body problems, and provides many features for creating animations. It implements high-speed visualization with OpenGL, and supports graphical user interface (GUI) for operations.
A collection of software tools for molecular dynamics calculations. Various interatomic potentials and tight binding models are implemented, and numerous external applications can be invoked. It also supports training and evaluation of GAP (Gaussian Approximation Potential), which is a form of machine learning potential.