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).
This application can produce input files of various applications for density functional theory (DFT) calculations via user-friendly parameter adjustment using three-dimensional computer graphics (3DCG) and graphical user interfaces (GUI). Input-file conversion between different applications is also possible.
An open-source application for first-principles molecular dynamics simulation based on pseudo-potential and plane-wave basis set. This application enables accurate molecular dynamics by density functional theory and Car-Parrinello method. It also supports structure optimization, Born-Oppenheimer molecular dynamics, path-integral molecular dynamics, calculation of response functions, the QM/MM method, and excited-state calculation.
A Python framework for easy creation, manipulation and optimization of quantum algorithms for NISQ (Noisy Intermediate Scale Quantum Computer). A simulator for the quantum processor in the Xmon architecture provided by Google has also been supported.
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.