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:
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 (), no later than 6/2/2023.
Agenda coming soon!