Organizers: KJ Schmidt (U Chicago), Arun Mannodi Kanakkithodi (Purdue), Ale Strachan (Purdue)

Experimental, data analysis, and simulation workflows and models are at the core of the daily activities of material researchers and engineers around the world. Making these workflows findable, accessible, interoperable, and reusable (FAIR) is critical to accelerate innovation and improve reproducibility. Making workflows and their associated data FAIR is critical to unleashing the power of machine learning and artificial intelligence.

Supported by the NSF’s Materials Research Coordinating Network (MaRCN), this workshop will consist of two main activities:

• Hands-on workshops to introduce materials researchers from academia, national labs, and industry to state-of-the-art FAIR workflows.
• Expert panel to discuss state-the-art, gaps, and possible collaborative work to move the community forward.

We have limited funds to support graduate students and early career researchers, please register using this link.