Cambridge Structure Database (CSD)

A fee-charging structure database of organic materials crystal structures. Three-dimensional structure data of small molecules and metal-organic crystals determined by the X-ray diffraction measurement can be downloaded. The data are compiled and distributed by the Cambridge Crystallographic Data Centre (CCDC).

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Crystallography Open Database

An open-access database of crystal structures. This database includes structural data of organic, inorganic, metal-organic compounds and minerals. At the end of 2017, this database contains about 400,000 entries. Three-dimensional structures of crystals can be viewed by GUI on the web.

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CHARMM

  • Level of openness 0 ☆☆☆
  • Document quality 2 ★★☆

A general-purpose application for molecular dynamics simulation equipped with many tools. This package was originally developed for biomolecules (peptides, proteins, nuclear acids, etc.), and the current version can perform molecular dynamics simulation for various systems such as solutions, crystals, membranes, and so on. It supports several sampling methods and calculation of free energy. It also supports various computing environments including both serial and parallel computers.

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ChemBio3D/Chem3D

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  • Document quality 2 ★★☆

A payware for modeling and visualizing molecules. This software includes a standard editor, ChemDraw, and can perform modeling from chemical structural formula. It implements structure optimization and molecular dynamics by molecular mechanics, and provides useful GUIs for MOPAC, Jaguar, GAMESS, and Gaussian. It can also perform spectroscopy analysis. It is included in high-end packages such as ChemBioOffice and ChemOffice.

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CRYSTAL

  • Level of openness 0 ☆☆☆
  • Document quality 3 ★★★

A first-principles simulation program based on the pseudopotential method utilizing Gaussian basis sets. It can perform simulations based on Hartree-Fock and density functional theories. It can be run under Unix/Linux, and also provides a simple GUI for Windows. Binaries are distributed for a fee, but users can first try the evaluation copy.

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CASTEP

  • Level of openness 0 ☆☆☆
  • Document quality 3 ★★★

Software for first-principles calculation based on pseudo-potential and plane-wave basis. This software performs electronic-state calculation of various systems by density functional theory, and can treat structure optimization, excited-state analysis, and so on. This software can be applied to many physical phenomena such as catalysis reaction, calculation of phase diagram, etc. There are many users of this payware in the world.

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CTM4XAS

  • Level of openness 2 ★★☆
  • Document quality 3 ★★★

An application for X-ray spectroscopy analysis based on atomic multiple-state calculation. This application performs multiplet calculation for transition-metal and rare-earth elements by taking into account effect of crystal fields and charge transfer, and can determine physical parameters by comparison between theory and experimental data via fitting. It implements useful graphical user interface(GUI), realizing intuitive operation.

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cdview

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

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.

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cuscalapack

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

GPU library for pdgemm and pzgemm, which are functions of matrix-matrix operations in ScaLAPACK.

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COMmon Bayesian Optimization Library (COMBO)

  • Level of openness 3 ★★★
  • Document quality 1 ★☆☆

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.

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