Software tool for constructing interatomic potentials based on nonlinear atomic cluster expansion. It requires the user to either prepare a fitting dataset based on pandas and ASE, or it can automatically extract data from VASP calculation results. The obtained potentials can be used for molecular dynamics simulations using LAMMPS, and it also provides the capability to calculate extrapolation grades for on-the-fly active learning.
An application for quantum chemical calculation based on DFTB (Density Functional based Tight Binding). This application performs structure
optimization and molecular dynamics by the DFTB force field as well as ordinary energy calculation, and implements parallel computing by OpenMP. A tool for visualization of molecular orbitals and an extended versions supporting MPI parallel computation or electron transport calculation by the nonequilibrium Green’s function method are also
available.
An application for prediction of stable and metastable structures from a chemical composition. This application applies particle swarm optimization to predict material structures from results of the first-principles calculation by external packages (VASP, CASTEP, Quantum Espresso, GULP, SIESTA, CP2k). It has been applied to predict not only three-dimensional crystal structures, but also those of clusters and surfaces.
An open-source application for visualization of many-particle systems. With simple operation by graphical user interface (GUI) or by command line, this application can visualize particle positions obtained from molecular dynamics simulation as well as three-dimensional scalar quantities such as potential energies. It supports various display options on kinds and shapes of particles, and can also visualize bond formation between particles.
A package including patches and scripts for adding transition-state calculation to the first-principles calculation application VASP. This package adds new functions to VASP such as calculation of reaction paths, transition-state structures, and rate constants, as well as a set of scripts for setting up calculations and analyzing results. A program for the Bader analysis for atomic charge assignment is also included.
A unified wrapper library for sequential and parallel versions of eigenvalue solvers. Sequential versions of dense-matrix diagonalization (LAPACK), parallel versions of dense-matrix diagonalization (EigenExa, ELPA, ScaLAPACK, etc.), and sequential/parallel versions of sparse-matrix diagonalization (SLEPc, Trilinos/Anasazi, etc.) can be installed quickly, and can be called from user’s program easily. Physical quantities written by eigenvalues or eigenvectors can also be evaluated by both sequential and parallel computation.
A library collection for numerical calculation of interacting quantum systems. Modern programming techniques are used in this library to implement common tasks for solving quantum impurity problems in dynamic mean-field theory in a simple and efficient way. It is written in C++ and Python, and includes tutorials using Jupyter Notebook.
A python package for automatic calculation of magnetic effective interactions between atoms (exchange and Dzyaloshinskii-Moriya interactions) from ab initio Hamiltonians based on Wannier functions and LCAO calculations. The package can postprocess Hamiltonians calculated using Wannier90, Siesta, and OpenMX. Input files for magnetic structure simulators such as Vampire can also be generated.
An open-source multi-purpose application for modeling and visualizing molecules (biomolecules, in particular). This application has been developed for multi-scale molecular simulation, and also provides a simple GUI for AMBER and Gaussian. It also implements exchange of protein residues and the Pathways model for the electron transfer in proteins. It calls rasmol for visualization of atoms and molecules.
A free software library for numerical diagonalization of quantum spin systems. Although the programs are based on TITPACK, they have been completely rewritten in C/C++ and several extensions have been added. It can handle, for example, the Heisenberg model, the Hubbard model, and the t-J model. This library supports dimension reduction of matrices exploiting symmetries, and it can run in parallel computing environments.