qmpy

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

Python library for the Open Quantum Materials Database, a first-principles computational database. qmpy supports several analysis tools such as crystal structures and phase diagrams. Users can perform automatic calculations using VASP.

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ChemSpider

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

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.

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PubChem

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

Open Chemistry database that has been in operation since 2004 under the National Institutes of Health (NIH) in the United States. It mainly targets data for small molecules, but information on large molecules such as lipids and peptides are also collected. The database can be accessed via web browser or PUG REST API. The data can be also downloaded from an FTP site.

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PubChemPy

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

Python code for a chemical database, PubChem. Users can search data in PubChem by compound name, structural information and so on. It is also possible to receive outputs as a Pandas DataFrame.

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Quimb

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

Easy-to-use and fast Python library for simulation of quantum information and quantum many-body systems. It provides Tensor module for tensor network simulations and Matrix module for “exact” quantum simulations.

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matminer

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

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.

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ChemDataExtractor

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

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.

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TurboGenius

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

Python wrapper to manage jobs for the ab initio Monte Carlo package TurboRVB. By combining with a workflow management application, TurboWorkflows,  users can perform high-throughput calculations based on TurboRVB.

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DAWN

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

Open-source software for analyzing scientific data. DAWN can visualize data in various dimensions, from 1D to 3D, and it is also possible to create maps that plot different types of data. It can not only visualize data, but also process data, such as fitting for peak detection. It supports general data formats such as text files and HDF5, as well as data formats such as NeXus, which is used in X-ray experiments.

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ABINIT-MP

  • Level of openness 2 ★★☆
  • Document quality 2 ★★☆
An application for quantum chemical calculation based on the fragment molecular orbital (FMO) method. This application can perform fast quantum chemical calculation of large molecules such as biopolymers, and includes graphical user interface (GUI) to help input-data preparation and analysis of simulation results. It also supports parallel computing from small clusters to massive parallel computers such as the Supercomputer Fugaku.
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