CCCM is a high-order CCM (coupled cluster method) code for lattice spin systems. It is possible to obtain the ground state and its energy of quantum spin systems in two or three dimensions.
CrySPY is a crystal structure prediction tool by utilizing first-principles calculations and a classical MD program. Only by inputting chemical composition, crystal structures can be automatically generated and searched. In ver. 0.6.1, random search, Bayesian optimization, and LAQA are available as searching algorithms. CrySPY is interfaced with VASP, Quantum ESPRESSO, and LAMMPS.
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.
GPU library for pdgemm and pzgemm, which are functions of matrix-matrix operations in ScaLAPACK.
COMmon Bayesian Optimization Library (COMBO) is an open source python library for machine learning techniques. COMBO is amenable to large scale problems, because the computational time grows only linearly as the number of candidates increases. Hyperparameters of a prediction model can be automatically learned from data by maximizing type-II likelihood.
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.
Program libraries for alloy modeling analysis using a cluster expansion method. Energy of alloy systems evaluated by other electronic state calculation libraries is used as an input, and atomic configuration effects are evaluated with the accuracy of a first principles calculation. Ground state structures, evaluation of thermodynamic quantities, equilibrium diagrams, disordering by temperature, etc. can be calculated with high accuracy.
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.
CONQUEST is a linear-scaling DFT (Density Functional Theory) code based on the density matrix minimization method. Since its computational cost, for both memory and computational costs, is only proportional to the number of atoms N of the target systems, the code can employ structure optimization or molecular dynamics on very large-scale systems, including more than hundreds of thousands of atoms. It also has high parallel efficiency and is suitable for massively parallel calculations.
ComDMFT is a massively parallel computational package to study the electronic structure of correlated-electron systems. Users can perform a parameter-free method based on ab initio linearized quasiparticle self-consistent GW (LQSGW) and dynamical mean field theory (DMFT).