MIL-OSI USA: Artificial Intelligence for Materials Science (AIMS) Workshop

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

Lodging Information