This workshop will be held at National Institute of Standards and Technology, Maryland, USA on August 1-2, 2019. Last year, we had over a hundred participants across different countries, universities, government labs, and industries.There is no registration fee for this event, but all attendees must be pre-registered to gain entry to the NIST campus.

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. Three key challenges in employing AI techniques to materials are: choosing effective descriptors for materials and choosing algorithm/work-flow during AI design and understanding the uncertainty in AI predictions. The idea of including physics-based models in the AI framework is also fascinating.  Lastly, 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.

Topics addressed in this workshop will include (but not be limited to):

• Application of classification/regression techniques for materials
• Selection and importance of features/descriptors for materials
• Comparison of AI techniques for materials
• Challenges applying AI to materials
• Dataset and tools for employing AI for materials
• Integrating experiments with AI techniques
• Using AI to develop classical force-fields

Some of the Invited speakers:

• Matthias Scheffler, Fritz-Haber-Institut der MPG Berlin
• Surya R. Kalidindi, Georgia Tech.
• Sergei V. Kalinin, Oak Ridge National Laboratory
• Karsten W. Jacobsen, Technical University of Denmark
• Stefano Curtarolo, Duke University
• Gus Hart, Brigham Young University
• Olexandr Isayev, University of North Carolina
• Murathan Aykol, Toyota Research Institute
• Ghanshyam Pilania, Los Alamos National Laboratory
• Zachary Ulissi, Carnegie Mellon University

Detailed webpage: https://www.ctcms.nist.gov/~knc6/aims/upcoming.html