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Materials Project

The Materials Project is a multi-institution, multi-national effort to compute the properties of all inorganic materials and provide the data and associated analysis algorithms for every materials researcher free of charge. The ultimate goal of the initiative is to drastically reduce the time needed to invent new materials by focusing costly and time-consuming experiments on compounds that show the most promise computationally.

Software

By computing properties of all known materials, the Materials Project aims to remove guesswork from materials design in a variety of applications. Experimental research can be targeted to the most promising compounds from computational data sets. Researchers will be able to data-mine scientific trends in materials properties. By providing materials researchers with the information they need to design better, the Materials Project aims to accelerate innovation in materials research.

Supercomputing

Supercomputing clusters at national laboratories provide the infrastructure that enables our computations, data, and algorithms to run at unparalleled speed. We principally use the Lawrence Berkeley National Laboratory's NERSC Scientific Computing Center and Computational Research Division, but we are also active with Oak Ridge's OLCF Argonne's ALCF and San Diego's SDSC

Screening

Computational materials science is now powerful enough that it can predict many properties of materials before those materials are ever synthesized in the lab. By scaling materials computations over supercomputing clusters, we have predicted several new battery materials which were made and tested in the lab. Recently, we have also identified new transparent conducting oxides and thermoelectric materials using this approach.

Contributors

The Materials Project thank all users for support and feedback. We are thankful to all our contributors who contribute to our software ecosystem. A complete list of contributors is listed here.

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  1. pymatgen Public

    Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials …

    Python 1.6k 887

  2. fireworks Public

    The Fireworks Workflow Management Repo.

    Python 379 188

  3. custodian Public

    A simple, robust and flexible just-in-time job management framework in Python.

    Python 149 110

  4. atomate2 Public

    atomate2 is a library of computational materials science workflows

    Python 200 107

  5. api Public

    New API client for the Materials Project

    Python 130 49

Repositories

Showing 10 of 52 repositories
  • pymatgen Public

    Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.

    Python 1,595 887 198 (2 issues need help) 36 Updated Mar 22, 2025
  • public-docs Public

    The latest documentation for the Materials Project.

    8 18 0 0 Updated Mar 21, 2025
  • atomate2 Public

    atomate2 is a library of computational materials science workflows

    Python 200 107 44 34 Updated Mar 20, 2025
  • MPContribs Public

    Platform for materials scientists to contribute and disseminate their materials data through Materials Project

    Jupyter Notebook 37 MIT 25 22 8 Updated Mar 19, 2025
  • emmet Public

    Be a master builder of databases of material properties. Avoid the Kragle.

    Python 59 69 42 15 Updated Mar 20, 2025
  • pymatgen-analysis-defects Public

    Defect analysis modules for pymatgen

    Python 47 12 2 1 Updated Mar 17, 2025
  • custodian Public

    A simple, robust and flexible just-in-time job management framework in Python.

    Python 149 MIT 110 22 3 Updated Mar 17, 2025
  • pymatgen-io-validation Public

    Comprehensive input/output validator. Made with the purpose of ensuring VASP calculations are compatible with Materials Project data, with possible future expansion to cover other DFT codes.

    Python 13 3 0 10 Updated Mar 17, 2025
  • maggma Public

    Building blocks for scientific data pipelines

    Python 39 33 39 7 Updated Mar 17, 2025
  • pyrho Public
    Python 40 9 2 3 Updated Mar 14, 2025