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.
Python tool for automatic extraction of chemical substance information from literature. Based on natural language processing algorithms, it can extract substance names and related physical/chemical properties such as melting points and spectra from documents written in English.
ChemSpider is a free chemical structure database that provides fast access to over 100 million structures, properties, and related information, and is operated by the Royal Society of Chemistry.
By integrating and linking compounds from hundreds of high-quality data sources, ChemSpider makes it easy to find chemical data from diverse data sources that are freely available for online searching. Users can also add and manage data in a wikipedia-like fashion. Meanwhile, manual curation by the Royal Society of Chemistry continuously improves data quality.
CIF2Cell is a tool to generate a crystal structure part of an input file of first-principles calculation software from crystal structure data file in CIF format. It supports various first-principles calculation codes such as ABINIT, Quantum Espresso, and VASP.
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.
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).
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 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.
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.
GPU library for pdgemm and pzgemm, which are functions of matrix-matrix operations in ScaLAPACK.