A python library for materials analysis. Flexible classes for representation of materials are prepared, and data for crystal structures and various material properties can be handled efficiently. This application can performs analysis of phase diagrams, Pourbaix diagrams, diffusion analyses etc. as well as electronic structure analyses such as density of states and band structures. This software is being actively developed keeping close relation with Materials Project.
An application for visualization of biopolymers. This application can visualize biopolymers by using its original command line and graphical user interface, more than 600 settings for visualization, and more than 20 visualization schemes. This application also supports more than 30 file formats such as PDB and multi-SDF, and can utilize sophisticated visualization methods such as the ray tracing.
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.
Python-based simulations of chemistry framework (PySCF) is a general-purpose electronic structure platform written in Python. Users can perform mean-field and post-mean-field methods with standard Gaussian basis functions. This package also provides several interfaces to other software such as BLOCK and Libxc.
A python package for the tight-binding method. PythTB supports tight-binding calculations of electronic structures and Berry phase in various kinds of systems. Users can use ab initio parameters obtained by Wannier90.
An interface package to use Torch (the open-source numerical library for machine learning) from Python. Users can easily implement deep learning based on neural networks, and can use various state-of-the-art methods. This package supports GPGPU parallel computation, and realises high-speed operation. A front-end interface for C++ is also prepared.