Open-source package for molecular dynamics simulation designed for biological macromolecules. This package can perform molecular dynamics simulation of biological macromolecules such as proteins, lipids, and nuclear acids as well as solutions by controlling temperature and pressure. This package can treat long-range interaction and free energy, and is designed for parallel computing.
An open-source library for machine learning. Various functions on deep learning based on neural network can be used by this package. This package is especially customised for image identification, and a number of sample codes are prepared. Users can also use pre-trained models, which are open in Caffe Model Zoo. Since this package is written in C++, high-speed operation is realised.
An open-source application for dynamical simulation of open quantum systems. It supports a wide range of Hamiltonians such as quantum optics, ion traps, and superconducting circuits. The time evolution of quantum states is evaluated by a master equation. These calculation library can be called from Python via a user-friendly interface.
An open-source library for machine learning. Various functions on machine learning/deep learning are implemented in this package. Using flexible user-friendly description, various types of networks from simple to complex ones can be implemented. GPGPU parallel computation based on CUDA is also supported.
An application for first-principles calculation based on density functional theory (DFT) optimized for X-ray spectroscopy analysis. Theoretical prediction and data fitting for X-ray spectroscopy such as XANES(X-ray absorption fine structure), XMCD(X-ray magnetic circular dichroism), RXD(resonant X-ray diffraction) can be preformes. This application employs a fully relativistic LSDA calculation based on the finite element method, and also supports the LDA+U method and the TD-DFT calculation.
Open source Python package for data mining of materials. It can extract data from more than dozens of databases, perform preprocessing and visualization of extracted data. By combining machine-learning tools such as scikit-learn, users can build machine-learning models with descriptors created from the extracted data.
An application for evaluating thermodynamic quantities and phase diagrams of alloys and compounds. This application can calculate thermal-equilibrium phase diagrams and thermodynamic quantities of alloys and compounds in combination with databases, and can be utilized for evaluation and prediction of physical properties in materials science and metallurgy. It supports various models of thermodynamics, and also includes useful tools for plotting phase diagrams.
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 tool for performing quantum many-body simulations based on dynamical mean-field theory. In addition to predefined models, one can construct and solve an ab-initio tight-binding model by using wannier 90 or RESPACK. We provide a post-processing tool for computing physical quantities such as the density of state and the momentum resolved spectral function. DCore depends on external libraries such as TRIQS and ALPSCore.
z-Pares is an app for obtaining the eigenvalues and eigenvectors for general sparse matrices using the contour integrals in the complex plane, i.e., Sakurai-Sugiura method. z-Parels is written in fortran 90/95 and supports the large scale parallelization via the two-level MPI distributed parallelism.