i-PI is a universal force engine interface written in Python, designed to be used together with an ab-initio (or force-field based) evaluation of the interactions between the atoms. This application includes a large number of sophisticated methods such as replica exchange molecular dynamics (REMD) and path integral molecular dynamics (PIMD). Inter-atomic forces can be computed by using external codes such as CP2K, Quantum ESPRESSO and LAMMPS.
A full-state vector simulator of quantum circuits optimized for multi-core and multi-nodes architectures. It provides C++ and Python interfaces. Also known as qHiPSTER (The Quantum High Performance Software Testing Environment).
An application for data analysis of X-ray absorption fine structure (XAFS). By interactive operation using a command line, experimental data of XAFS can be analyzed by various analysis methods. This application also supports various useful functions such as high-speed Fourier analysis, fitting in the radial/k-space coordinates, and data plotting.
A pre/post-processing application for SIESTA and TranSIESTA. This application can calculate phonon frequencies, electron-phonon coupling, and contributions of inelastic scattering to the conductance. It also provides a Python interface for accessing data in the Hamiltonian output from SIESTA.
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).
isqpr is an R package to find candidate molecules that has your desired chemical structures and chemical properties. SMILES (Simplified Molecular Input Line Entry Specification Syntax) is employed to represent chemical structures. To find candidate molecules, sequential Monte Carlo method generates new molecules, whose chemical properties are predicted by machine learning techniques.
A commercial database of inorganic crystal structures. This database is run by FIZ Karlsruhe. 181,000 crystal structure data are registered as of March 2016. 6,000 crystal structure data are added per year on average, and data are updated twice per year based on data in published scientific journals.