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.
Python/C++ based software package that employs deep learning techniques for construction of interatomic potentials. It implements the Deep Potential, which defines atomic environment descriptors with respect to a local reference frame. The output of many first-principles and molecular dynamics applications can be used as training data, and the trained potentials can be used for molecular dynamics calculations using LAMMPS and path integral molecular dynamics calculations using i-PI.
An open source application to simulate crystal structures and to calculate and refine against diffraction pattern and the pair distribution function. A special emphasis placed is on the simulation of materials with disorder and the package provides many tools to create and distribute defects throughout the crystal. Another strong feature is the simulation of nanoparticles.
An open-source application for simulation based on the density-matrix renormalization group (DMRG). This application can perform high-speed calculation of low-dimensional quantum systems with high accuracy. It implements generic programming techniques in the C++ language, and can easily extend simulation to new models and geometries. It is developed putting emphasis on user-friendly interfaces and low dependences on environments.
DSQSS is an application program for solving quantum many body problems in a discrete set (typically a lattice). It carries out quantum Monte Carlo simulations that sample from the Feynman path integral using the worm update. It can handle any lattice geometry and interaction.
An open-source application for first-principles calculation utilizing the DV-Xα method. It produces electronic structure for a wide rage of physical systems such as atoms, molecules and crystals. The DV-Xα method realizes high-speed computation for all-electron calculations, and makes it possible to evaluate various physical properties and electron transition probability (especially of core-electron excitation). Tools for supplying input data, and visualizing and post-processing output data are also released.