TC++

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

TC++ is open-source software for ab initio calculations using the transcorrelated (TC) method. In TC++, users can take account of electron correlations in a Jastrow correlation factor based on the TC method. Electronic structures obtained by Quantum ESPRESSO can be used as an initial state of TC++.

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OQMD: The Open Quantum Materials Database

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

A database for thermodynamic properties and crystal structures calculated based on the density functional theory by a research group in Northwestern University. OQMD provides over one million data generated by using not only experimental crystal structures provided by ICSD but also those obtained by calculations. Users can search data in OQMD by using Python API.

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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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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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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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AFLOW (Automatic-FLOW)

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

A highly efficient framework for crystal structure exploration and property prediction dedicated to material science calculations. This application can automate the setup, execution, and analysis of the results of calculations based primarily on the density functional theory. It provides data on more than millions of crystal structures and can be used for high throughput calculations for material exploration. It also interfaces with various DFT codes (VASP, Quantum ESPRESSO, etc.).

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ShengBTE

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

A Boltzmann transport equation solver for calculating lattice thermal conductivity based on phonon information obtained from first-principles calculations. It takes into account three-phonon interactions and enables first-principles analysis of thermal transport properties in solids, including anisotropic crystals, complex structures, and those containing defects. Tutorials and input-support tools are also provided. A tool for calculating third-order force constants (thirdorder.py) is also available on the same website.

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FourPhonon

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

A software package for calculating lattice thermal conductivity based on phonon information obtained from first-principles calculations, including four-phonon scattering processes. It extends ShengBTE to account for four-phonon interactions that become dominant at high temperatures. The program enables quantitative analysis of the competition between three- and four-phonon interactions as well as temperature dependence, allowing for more accurate evaluation of thermal transport properties.

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Matbench Discovery

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

A benchmark framework for evaluating general-purpose, i.e., universal, machine learning potentials, along with a leaderboard based on those evaluations. Rankings are determined by a comprehensive assessment that considers the accuracy of predicted formation energy of materials, structural relaxation, and thermal conductivity. Recently, in addition to public research institutions such as universities, major companies like Meta, Microsoft, and Google have also joined the development of universal potentials, taking top positions on the leaderboard.

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