A set of routines for real-symmetric dense eigenproblems in supercomputers or massively parallel machines. Both of standard and general eigenproblems are supported. A fast computation is achieved by optimal hybrid solvers among eigenproblem libraries of ELPA, EigenExa and ScaLAPACK. The package includes a mini-appli that can be used in a benchmark test.
An open-source application for ab initio quantum chemical calculation. This application performs electronic structure calculation of molecules by the Hartree-Fock, density functional, many-body perturbation, configuration interaction theories, and so on. Even though this application is freeware, it succeeds in maintaining high-quality and high-performance codes by active development, and has a number of world-wide users. It histrically shares core programs with GAMESS-UK.
A low-energy solver for a wide ranger of quantum lattice models (multi-orbital Hubbard model, Heisenberg model, Kondo-lattice model) by using variational Monte Carlo method. User can obtain high-accuracy wave functions for ground states of above models. Users flexibly choose the correlation factors in wavefunctions such as Gutzwiller, Jastrow, and doublon-holon binding factors and optimize more the ten thousand variational parameters. It is also possible to obtain the low-energy excited states by specifying the quantum number using the quantum number projection.
RSDFT is an ab-initio program with the real-space difference method and a pseudo-potential method. Using density functional theory (DFT), this calculates electronic states in a vast range of physical systems: crystals, interfaces, molecules, etc. RSDFT is suitable for highly parallel computing because it does not need the fast Fourier transformation. By using the K-computer, this program can calculate the electronic states of around 100,000 atoms. The Gordon Bell Prize for Peak-Performance was awarded to RSDFT in 2011.
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.
aenet is software for atomic interaction potentials using artificial neural networks. Users can construct neural network potentials using structures of target materials and their energies obtained from first principle calculations. The generated potentials can be used to molecular dynamics or Monte Carlo simulations.
H-wave is a Python package for performing unrestricted Hartree-Fock (UHF) calculations and random phase approximation (RPA) calculations for itinerant electron systems. H-wave supports UHF calculations both in real- and wavenumber-spaces. H-wave supports one-body and two-body interactions in the Wannier90 format as inputs for H-wave, and thus users can solve ab initio effective Hamiltonians derived from Wannier90/RESPACK calculations based on UHF and RPA methods.
An application for visualization of Fermi surfaces.
This application displays Fermi surfaces colored as a function of an arbitrary scalar quantities such as magnitude of Fermi velocities and superconducting gap. It only requires a minimum set of data to draw Fermi surfaces. FermiSurfer provides a simple graphical user interface; the user can smoothly turn on/off the stereogram, nodal-lines, etc.
The fragment molecular orbital (FMO) method can efficiently do quantum-mechanical calculations of large molecular systems by splitting the whole system into small fragments. The FMO program is distributed within quantum-chemical program suite GAMESS-US. FMO can provide various information regarding the structure and function of biopolymers, such as the interaction between a protein and a ligand.
A C++ library for implementing a tensor product wavefunction method to simulate many-body electron systems. This library provides a useful environment for simple definition of tensors in programs, and supports functions of linear algebras and quantum number conservation needed in a tensor network method. This library keeps excellent flexibility and efficiency in maintenance, and can easily make a solver of one-dimensional electron systems such as density-matrix renormalization group (DMRG).