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Accelerated data-driven materials science with the Materials Project

Nature Materials volume 24, pages 1522–1532 (2025)Cite this article

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Abstract

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

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Fig. 1: Growth of the MP in materials properties and users since its inception.
Fig. 2: The MP ecosystem of open-source software libraries.
Fig. 3: Examples of compounds predicted to have target properties by leveraging the data and analysis tools of the MP.

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Acknowledgements

This work was intellectually led by the Materials Project program KC23MP, supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no. DE-AC02-05-CH11231. The Materials Project Collaboration includes the authors of this manuscript in addition to current and previous members of the Materials Project program, for example, developers of workflows for the prediction of various properties, plus contributors from the broader community acknowledged in this manuscript. We thank all users of the MP for their support and feedback. We thank all contributors to the MP software stack, without whom the MP would not be possible. A complete and up-to-date list of contributors is publicly available at GitHub (https://github.com/materialsproject#contributors). This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BES-ERCAP 0032604. A.S.R. acknowledges support via a Miller Research Fellowship from the Miller Institute for Basic Research in Science, University of California, Berkeley.

Author information

Authors and Affiliations

  1. Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Matthew K. Horton, Patrick Huck, Ruo Xi Yang, Jason M. Munro, Shyam Dwaraknath, Tyler S. Mathis, Aaron D. Kaplan, Janosh Riebesell, Andrew S. Rosen, Matthew J. McDermott, Orion A. Cohen, Matthew C. Kuner, Sinead M. Griffin, Jeffrey B. Neaton, Daryl C. Chrzan, Mark Asta, Gerbrand Ceder & Kristin A. Persson

  2. Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, USA

    Matthew K. Horton, Andrew S. Rosen, Orion A. Cohen, Matthew C. Kuner, Daryl C. Chrzan, Mark Asta, Gerbrand Ceder & Kristin A. Persson

  3. Department of Chemistry, Imperial College London, London, UK

    Alex M. Ganose

  4. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

    Ryan S. Kingsbury

  5. Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ, USA

    Ryan S. Kingsbury

  6. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China

    Mingjian Wen

  7. Lawrence Livermore National Laboratory, Livermore, CA, USA

    Jimmy X. Shen

  8. Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Karlo Berket & Shreyas Cholia

  9. Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Janosh Riebesell

  10. Federal Institute for Materials Research and Testing, Berlin, Germany

    Janine George

  11. Friedrich Schiller University Jena, Jena, Germany

    Janine George

  12. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Evan W. C. Spotte-Smith

  13. Energy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Alex Dunn & Anubhav Jain

  14. Université catholique de Louvain, Louvain-la-Neuve, Belgium

    Gian-Marco Rignanese

  15. Matgenix SRL, Charleroi, Belgium

    Guido Petretto & David Waroquiers

  16. Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Sinead M. Griffin

  17. Department of Physics, University of California, Berkeley, Berkeley, CA, USA

    Jeffrey B. Neaton

  18. Department of Engineering, Dartmouth University, Hanover, NH, USA

    Geoffroy Hautier

  19. Department of Nanoengineering, University of California San Diego, San Diego, CA, USA

    Shyue Ping Ong

Authors
  1. Matthew K. Horton
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  2. Patrick Huck
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  3. Ruo Xi Yang
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  4. Jason M. Munro
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  5. Shyam Dwaraknath
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  8. Mingjian Wen
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  9. Jimmy X. Shen
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  10. Tyler S. Mathis
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  11. Aaron D. Kaplan
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  12. Karlo Berket
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  14. Janine George
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  15. Andrew S. Rosen
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  16. Evan W. C. Spotte-Smith
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  17. Matthew J. McDermott
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  18. Orion A. Cohen
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  19. Alex Dunn
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  20. Matthew C. Kuner
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  21. Gian-Marco Rignanese
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  22. Guido Petretto
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  23. David Waroquiers
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  24. Sinead M. Griffin
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  25. Jeffrey B. Neaton
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  26. Daryl C. Chrzan
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  27. Mark Asta
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  28. Geoffroy Hautier
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  29. Shreyas Cholia
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  30. Gerbrand Ceder
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  31. Shyue Ping Ong
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  32. Anubhav Jain
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  33. Kristin A. Persson
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Corresponding author

Correspondence to Kristin A. Persson.

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Nature Materials thanks Giulia Galli, Matthias Scheffler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Horton, M.K., Huck, P., Yang, R.X. et al. Accelerated data-driven materials science with the Materials Project. Nat. Mater. 24, 1522–1532 (2025). https://doi.org/10.1038/s41563-025-02272-0

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