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Harnessing the Materials Project for machine-learning and accelerated discovery

  • Weike Ye (a1), Chi Chen (a2), Shyam Dwaraknath (a3), Anubhav Jain (a4), Shyue Ping Ong (a5) and Kristin A. Persson (a6)...


Improvements in computational resources over the last decade are enabling a new era of computational prediction and design of novel materials. The resulting resources are databases such as the Materials Project (, which is harnessing the power of supercomputing together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials, to design novel materials, and to make the data available for free to the community, together with online analysis and design algorithms. The current release contains data derived from quantum mechanical calculations for more than 70,000 materials and millions of associated materials properties. The software infrastructure carries out thousands of calculations per week, enabling screening and predictions for both novel solids as well as molecular species with targeted properties. As the rapid growth of accessible computed materials properties continues, the next frontier is harnessing that information for automated learning and accelerated discovery. In this article, we highlight some of the emerging and exciting efforts, and successes, as well as current challenges using descriptor-based and machine-learning methods for data-accelerated materials design.



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Harnessing the Materials Project for machine-learning and accelerated discovery

  • Weike Ye (a1), Chi Chen (a2), Shyam Dwaraknath (a3), Anubhav Jain (a4), Shyue Ping Ong (a5) and Kristin A. Persson (a6)...


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