An open-source application for visualization developed for input and output of GAMESS. This application supports various types of input formats such as GAMESS, XYZ, MolDel, pdb, and CML as well as input by GUI and the Z-matrix format. It can visualize molecular orbitals, electron densities, electrostatic potentials, and normal modes, and can output results into various formats.
A Python framework for easy creation, manipulation and optimization of quantum algorithms for NISQ (Noisy Intermediate Scale Quantum Computer). A simulator for the quantum processor in the Xmon architecture provided by Google has also been supported.
A python library for pre- and post-processing of first-principles electronic structure calculations. As a pre-processing tool, it can automatically generate k-point pathways for first-principles calculations of band structures based on the crystal symmetry. It can also post-process first-principles calculation results to generate band structure and density of states plots with atomic species and orbital contributions, or visualize spin textures and Fermi surfaces. It also provides a functionality for band unfolding.
An open-source application for atomic structure analysis from powder diffraction data. This application can calculate atomic coordinates, valence sums, and chemical bonds from diffraction data of crystals, nanostructures, and amorphous materials. It is written in Python, and realizes multi-functional fitting and flexible data analysis.
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.