• Workshop: Artificial Intelligence for Materials Science (AIMS)

    August 1-2, 2019

  • The Materials Genome Initiative (MGI) promises to expedite materials discovery through high-through computation and high-throughput experiments. While the MGI effort has been successful to screen interesting materials among thousands of materials, the possible materials can span up to 10100 limiting the current MGI philosophy.

    One of the possible approaches to deal with this problem is using artificial-intelligence (AI) tools such as machine-learning, deep-learning and various optimization techniques to efficiently evaluate materials performance. Although AI has been very successful in fields such as voice-recognition, self-driving cars, language translation etc., its applicability to materials design is still in its developing phase. Two key challenges in employing AI techniques to materials are: choosing effective descriptors for materials and choosing algorithm/work-flow during AI design. The idea of including physics-based models in the AI framework is also fascinating. Lastly, uncertainty quantification in AI based predictions for material properties and issues related to building infrastructure for disseminating AI knowledge are of immense importance for making AI based investigation of materials successful. This workshop is intended to cover all the above-mentioned challenges. To make the workshop as effective as possible we plan to mainly focus on inorganic solid-state materials, but are not limited by it.

    Information for participants:

    Please note that the seats are limited and priority would be given based on the date of your registration and the registration closes on July 25. We are expecting around 150 participants, so please arrive at the NIST gate between 7:30-8:00 AM. The workshop won't cover travel and living expenses of the attendees. We recommend 46 inch x 46 inch size for the posters. Participants should bring their laptops for the hands-on session. We will be using jarvis-tools and Google-colab for the hands-on session, but you may also install the jarvis-tools package on your laptop from https://github.com/usnistgov/jarvis .

    NIST employees/associates do not need to register, just walk in. There will be a VTC connection to NIST-Boulder. We have reserved room 1-4072 (7:00 AM-2:30 PM) there both days.

    • bookmark_borderApplication of classification/regression techniques
    • bookmark_borderApplication of physics-based constraints
    • bookmark_borderSelection and importance of features/descriptors
    • bookmark_borderComparison metrics of AI techniques
    • bookmark_borderChallenges applying AI to materials
    • bookmark_borderDataset and tools for employing AI
      .
    • bookmark_borderIntegrating experiments with AI techniques
    • bookmark_borderUsing AI to develop classical force-fields
  • user bg
    visibility Matthias Scheffler

    perm_identityFritz-Haber-Institut der MPG Berlin

    user bg
    visibility Sergei V. Kalinin

    perm_identityOak Ridge National Laboratory

    user bg
    visibility Nicola Marzari

    perm_identityÉcole polytechnique fédérale de Lausanne

    user bg
    visibility Tim Mueller

    perm_identityJohns Hopkins University

    user bg
    visibility Surya R. Kalidindi

    perm_identityGeorgia Tech.

    user bg
    visibility Gus Hart

    perm_identityBrigham Young University

    user bg
    visibility Olexandr Isayev

    perm_identityUniversity of North Carolina

    user bg
    visibility Stefano Curtarolo

    perm_identityDuke University

    user bg
    visibility Deyu Lu

    perm_identityBrookhaven National Lab

    user bg
    visibility Muratahan Aykol

    perm_identityToyota Research Institute

    user bg
    visibility Ghanshyam Pilania

    perm_identityLos Alamos National Laboratory

    user bg
    visibility Zachary Ulissi

    perm_identityCarnegie Mellon University

    user bg
    visibility Karsten W. Jacobsen

    perm_identityTechnical University of Denmark

    user bg
    visibility Changning Niu

    perm_identityQuesTek Innovations

    user bg
    visibility Shyue Ping Ong

    perm_identityUniversity of California San Diego

  • user bg
    visibility Kamal Choudhary

    perm_identity NIST, Gaithersburg

    email kamal.choudhary@nist.gov

    Kamal Choudhary close

    perm_identity Material Scientist

    business NIST

    location_on Gaithersburg, Maryland

    user bg
    visibility Francesca Tavazza

    perm_identityNIST, Gaithersburg

    email francesca.tavazza@nist.gov

    Francesca Tavazza close

    perm_identity Material Scientist

    business NIST

    location_on Gaithersburg, Maryland

    user bg
    visibility Carelyn Campbell

    perm_identityNIST, Gaithersburg

    email carelyn.campbell@nist.gov

    Carelyn Campbell close

    perm_identity Material Scientist

    business NIST

    location_on Gaithersburg, Maryland

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