Artificial Intelligence for Materials Science (AIMS) Workshop
Credit: Crissy Robinson/NIST
As part of the JARVIS workshop series, the 6th Artificial Intelligence for Materials Science (AIMS) workshop will be held as an in-person only event at the National Cybersecurity Center of Excellence (NCCoE) located at 9700 Great Seneca Highway in Rockville, Maryland on July 9 - 10, 2025. This event is sponsored by the National Institute of Standards and Technology (NIST).
The scope of the workshop is briefly stated below:
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, challenges and advantages of self-driving laboratories, 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):
- Datasets and tools for employing AI for materials
- Integrating experiments with AI techniques
- Graph neural networks for materials
- Comparison of AI techniques for materials
- Challenges of applying AI to materials
- Uncertainty quantification and building trust in AI predictions
- Generative modeling
- Foundation models
- Machine learning force fields
- Large language models
- Autonomous experimentation
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 June 27, 2025. We plan to hold a best poster competition for early career researchers.
List of Speakers
| Jiaman Hu | Wisconsin |
| Tess Smidt | MIT |
| Brandon Wood | Meta |
| Heather Kulik | MIT |
| Joseph Krause | Radical AI |
| Ichiro Takeuchi | UMD |
| Martin Seifrid | NC State |
| Olexandr Isayev | CMU |
| Ali Hamze | Samsung |
| Simon J.L. Billinge | Columbia |
| Ankit Agrawal | Northwestern |
| Jason Hattrick-Simpers | University of Toronto |
| Arun Mannodi-Kanakkithodi | Purdue |
| Benji Maruyam | AFRL |
| Panchapakesan Ganesh | ORNL |
| Roberto Car | Princeton |
| Shengyen Li | NIST |
| Aditya Nandy | UCLA |
| Steven Torrisi | Toyota |
| Olga S. Ovchinnikova | Thermo Fisher Scientific |
| Milad Abolhasani | NC State University |
| Luis Barroso-Luque | Meta |
| Nathan Johnson | ZEISS |
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