Artificial Intelligence for Materials Science (AIMS) Workshop

As a part of the JARVIS workshop series, NIST is organizing the 4th Artificial Intelligence for Materials Science (AIMS) workshop that will be held virtually, on July 25 - 27, 2023. The Materials Genome Initiative (MGI) promises to expedite materials discovery through the use of data from high-throughput computation and high-throughput experiments. The application of artificial-intelligence (AI) tools such as machine-learning, deep-learning and various optimization techniques is critical to achieving such a goal.

Some of the key activity areas in applying AI techniques to materials are: developing well-curated and diverse datasets, choosing effective representations for materials, inverse materials design, integrating autonomous experiments and theory, and choosing appropriate algorithms/work-flows. The inclusion of physics-based models into an AI framework is also a major research direction.  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 focus mainly but not exclusively on inorganic solid-state materials.

Some of the topics addressed in this workshop will include:

1. Datasets and tools for employing AI for materials
2. Integrating experiments with AI techniques
3. Graph neural network
4. Comparison of AI techniques for materials
5. Challenges applying AI to materials
6. Uncertainty quantification and building trust in AI predictions
7. Generative modeling
8. Using AI to develop classical force-fields
9. Natural language processing
10. AI-guided autonomous experimentation

Call for Abstracts 

If registered participants are interested in presenting a short (~12 minute) lightning talk, please send name, affiliation, title, and abstract to daniel.wines [at] nist.gov (daniel[dot]wines[at]nist[dot]gov), no later than 6/2/2023.

Confirmed Speakers

• Maxim A Ziatdinov (Oak Ridge National Lab)
• Jed Pitera (IBM)
• Brenda Rubenstein (Brown)
• Rachel Kurchin (Carnegie Mellon)
• Arun Kumar Mannodi Kanakkithodi (Purdue)
• Jason Hattrick Simpers (Toronto)
• Zi-Kui Liu (Penn State)
• Addis Fuhr (Oak Ridge National Lab)
• Keqiang Yan (TAMU)
• Victor Fung (Georgia Tech)
• Rafael Gomez-Bombarelli (MIT)
• Erik Thiede (Flatiron Institute)
• Vidushi Sharma (IBM)
• Taylor D. Sparks (Utah)
• Tonio Buonassisi (MIT)
• Connor W. Coley (MIT)
• Andrew White (Future House, Inc.)
• Joseph Montoya (Toyota Research Institute)

Organizing Committee

• Daniel Wines
• Kamal Choudhary
• Francesca Tavazza
• Brian DeCost
• Austin McDannald
• Kevin Garrity
• Howie Joress