AIMS is a workshop aimed at getting together experts from industry, academia, and government to facilitate highly technical dialogue on the intersection of AI and materials science. Some of the key research areas for materials AI that will be discussed at the meeting are: 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/workflows. 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 of these challenges.
Agenda coming soon
June 16, 2026 | 9:00am -5:30pm ET
June 17, 2026 | 8:30am - 4:00pm ET
| Name | Affiliation |
|---|---|
| Aditi Krishnapriyan | Berkeley |
| Akshay Talekar | UL |
| Cate Brinson | Duke |
| Danish Khan | CalTech |
| David Elbert | Johns Hopkins |
| Deyu Lu | Brookhaven |
| Dung-Yi Wu | GE |
| Fei Zhou | LLNL |
| Jason Hattrick-Simpers | University of Toronto |
| Jeff Ting | Nanite |
| Jie Xu | Argonne |
| Josh Schrier | Fordham |
| Justin Smith | NVIDIA |
| Keith Task | BASF |
| Linda Hung | Toyota Research Institute |
| Michael Taylor | LANL |
| Mitra Taheri | Johns Hopkins |
| Nina Andrejevic | Argonne |
| Peter Beaucage | Lila |
| Remi Dingreville | Sandia |
| Stefano Falletta | Radical AI |
| Tim Erdmann | IBM |
| Tyler Martin | NIST |
| Wesley Reinhart | Penn State |
| William Ratcliff | NIST |
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