An open-source application for first-principles calculation utilizing pseudo-potentials and atom-localized basis sets. This application is capable of performing electronic structure calculations, structural relaxation, and molecular dynamics in a wide range of systems based on density functional theory. By adopting atom-localized basis sets, it realizes high-speed electronic calculation and linear-scaling in suitable computer systems. It can also perform electronic conductance calculations based on non-equilibrium Green’s function method.
Python library for the design, simulation, and optimization of continuous-variable quantum optical circuits. It has high-level functions for solving problems including graph and network optimization, machine learning, and chemistry, and can perform training and optimization of quantum programs using the TensorFlow backend.
A python tool for generating symmetry-inequivalent supercell structures from a CIF file containing site occupancy information. SHRY can be used as a command-line tool as well as a module in a python script.
A comprehensive online database for materials science. It covers 3,000 kinds of property information (crystal structure, phase diagrams, thermophysical property data, etc.) and 290,000 kinds of material data and provides efficient information search for these data. A variety of analytics tools, including data integration, graphing and customizable data visualization, are also available.
An open-source application for evaluating superconducting gaps from resutls of the first-principles calculation by Quantum ESPRESSO. By calculating electron-phonon interaction and screened Coulomb interaction from the first-principles calculation, superconducting gaps can be obtained from the gap equation. Quasiparticle densities of states and ultrasonic attenuation rates can also be calculated.
An open-source library for data mining and data analysis. This package implements various methods of machine learning such as supervised learning (data classification, data regression, etc.), unsupervised learning (data clustering, etc.), and data pre-processing. This package is implemented on Python numerical libraries, NumPy and Scipy, and supports parallel computation.