RSPACE is a first-principles code package based on a real-space finite-difference pseudo-potential method. It computes electronic states with high-speed and high precision in aperiodic systems of surfaces, solid interfaces, clusters, nanostructures, and so forth. It provides large-scale computing for semiconductor devices of nanostructure surface and interface reactions, calculation of transport properties in semi-infinite boundary conditions, and a massively parallel computing using the space partitioning method.
An open-source application for the first-principles calculation based on the all-electron method with localized bases. By adopting the full-potential LMTO method, high-speed electronic state calculation can be performed with a less number of bases compared with the standard all-electron method. There is no restriction on symmetries as in the LMTO-ASA method, and spin polarization and spin-orbit interaction can also be treated.
FORTRAN-based software package developed by the Behler Group for implementing Behler-Parinello neural network potentials. Potentials can be constructed, evaluated, and used for molecular dynamics simulations using LAMMPS. The newest generation of neural network potentials that take into account long-range electrostatic interactions are implemented.
Photo-excited electron dynamics simulator based on time-dependent density functional theory using real-time, real-space grids. It can perform calculations of linear photo-response and nonlinear photo-response to pulse radiation in a variety of systems including isolated systems, periodic systems, interfaces/surfaces, etc. It can perform massively parallel calculations in systems consisting of thousands of atoms, and it can also perform multiscale simulation of electron-electromagnetic field-coupled dynamics.
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.
A tool for generating wavevector paths in band calculations of solids. It identifies high-symmetry points in reciprocal space based on the symmetry of the crystal and provides a standardized “path” connecting them. It supports various crystal structure formats (such as POSCAR and CIF) and is compatible with many electronic structure calculation software (e.g., VASP, Quantum ESPRESSO, ABINIT). A web-based interface is also available.
A Boltzmann transport equation solver for calculating lattice thermal conductivity based on phonon information obtained from first-principles calculations. It takes into account three-phonon interactions and enables first-principles analysis of thermal transport properties in solids, including anisotropic crystals, complex structures, and those containing defects. Tutorials and input-support tools are also provided. A tool for calculating third-order force constants (thirdorder.py) is also available on the same website.
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.
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.
Software package to implement Behler-Parinello neural network potentials. Potentials can be trained from structure-energy/ interatomic forces/stress data, and molecular dynamics calculations using LAMMPS can also be performed using learned potentials. A prediction uncertainty measure can also be calculated simultaneously.