2024 Artificial Intelligence for Materials Science (AIMS) Workshop

As a part of the JARVIS workshop series, NIST is sponsoring the 5th Artificial Intelligence for Materials Science (AIMS) workshop. The workshop will be held in-person only at the National Cybersecurity Center of Excellence (NCCoE), located at 9700 Great Seneca Highway, Rockville, MD 20850, from July 17 - 18, 2024. 

The Materials Genome Initiative (MGI) promises to expedite materials discovery through 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 research areas for materials AI include: developing well-curated and diverse datasets, choosing effective representations for materials, inverse materials design, integrating autonomous experiments and theory, merging physics-based models with AI models, and choosing appropriate algorithms/work-flows. 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 materials investigation successful. This workshop is intended to cover all the above-mentioned challenges. To make the workshop as effective as possible we plan to largely but not exclusively focus on inorganic solid-state materials.
 

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

1)  Datasets and tools for employing AI for materials

2) Integrating experiments with AI techniques

3) Graph neural networks for materials

4) Comparison of AI techniques for materials

5) Challenges of 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/Large language models

CALL FOR POSTERS 

If registered participants are interested in presenting a poster, please send name, affiliation, title and abstract to daniel.wines [at] nist.gov (daniel[dot]wines[at]nist[dot]gov) no later than 5/31/2024.

CONFIRMED SPEAKERS

• Anouar Benali (Argonne National Lab)
• Eddie Kim (Cohere)
• Christopher Sutton (South Carolina)
• Hongliang Xin (Virginia Tech)
• Nicola Marzari (EPFL)
• Sergei Kalinin (University of TN, Knoxville)
• Chris Stiles (JHUAPL)
• Yongqiang Cheng (ORNL)
• Mathew Cherukara (Argonne National Lab)
• Olga Wodo (University at Buffalo)
• Ming Hu (South Carolina)
• Keqiang Yan (TAMU)
• P. Ganesh (ORNL)
• Rama Vasudevan (ORNL)
• Guido von Rudorff (University of Kassel)
• Maria Chan (Argonne National Lab)
• Vidushi Sharma (IBM)
• Debra Audus (NIST)
• Dilpuneet Aidhy (Clemson)
• Michael Waters (Northwestern)
• Anuroop Sriram (Meta)

co-organizers

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