Fortran codes for computing the specified k-th eigenvalue and eigenvector for generalized symmetric definite eigenvalue problems. Sylvester’s law of inertia is employed as the fundamental principle in computations, and the sparse direct linear solver (MUMPS) is used in the main routine. By inputting Hamiltonian and its overlap matrices, user can compute electron’s energy and its wave function in the specified k-th energy level.
An open-source numerical library for machine learning. Using other machine learning numerical libraries (TensorFlow, CNTK, Theano, etc.), users can construct neural networks by relatively short codes. Since a number of methods in machine learning and deep learning are implemented, users can try state-of-the-art methods easily. This package is written by Python.
Application for specifying and simulating lattice kinetic Monte Carlo models. It has been developed in the context of simulating heterogeneous catalysis. Models can be specified using provided python APIs or through a simple GUI.
Kω implements large-scale parallel computing of the shifted Krylov subspace method. Using Kω, dynamical correlation functions can be efficiently calculated. This application includes a mini-application for calculating dynamical correlation functions of quantum lattice models such as the Hubbard model, the Kondo model, and the Heisenberg model in combination with the quantum lattice solver of quantum many-body problems, HΦ.
An open-source program package for numerical diagonalization based on the Lanczos method, specialized for spin chains with unit spin magnitude, S=1. This package, which uses another open-source program package, TITPACK, calculates eigenenergies and eigenvectors of ground states and low-lying excited states of spin chains with finite length. By the subspace partitioning method, both memory and cpu-time requirements are considerably reduced.
An open-source Python package for calculation of quantum transport properties. Based on tight-binding models, this application can perform high-speed calculation of various transport properties such as conductance, current noise, and density of states. It can describe geometries of physical systems flexibly and easily, and can also treat superconductors, ferromagnetic materials, topological matters, and graphene.