Cryo-EM/ET is one of the research areas where gaining a deeper understanding of the workflows, research computing and data requirements, collaborations, and challenges will enable the ERN to have the broadest impact across multiple research disciplines, pedagogical approaches, senior level college and university administrators, and other organizations within the region and beyond. We estimate that the Cryo-EM/Cryo-ET community in the Northeast comprises nearly 50 centers serving more than 800 laboratories from Pennsylvania to Maine. Applications of Cryo-EM span structural biology and material science. Single Particle Reconstruction information produced by these centers is producing transformative insights. Given the cost and value of the instruments involved, fast turnaround and efficient use of resources is key. While all centers are well equipped to deliver images from prepared samples, processing and storage of these images can present significant and unnecessary obstacles, especially for labs that do not have easy access to computing resources and expertise. The Cryo-EM/Cryo-ET microscopy labs in the Northeast have formed a relatively tight knit community, allowing for free flow of information and experience, and reducing duplication of effort, and accelerating the adoption of new techniques. This session will explore possibilities for extending this collaboration to include the community of Research Computing and Networking organizations that serve these labs and the broader impacts and to form a working group to focus on future workshops and content for the Mid-Scale proposal.

Materials Discovery is one of the research areas where gaining a deeper understanding of the workflows, research computing and data requirements, collaborations, and challenges will enable the ERN to have the broadest impact across multiple research disciplines, pedagogical approaches, senior level college and university administrators, and other organizations within the region and beyond. Researchers in materials discovery are realizing that their traditional data-intensive HPC workflows are reaching the limits of spatial and temporal scales required to make deeper insights and predictions. For this reason, they are looking to new paradigms that include convergence of HPC and Machine Learning (ML) methodologies, algorithm development, and novel ways to access the data distributed across multiple institutions used in training systems as promising approaches to overcome the major computational performance limitations. Materials Discovery offers an attractive testbed for advanced cyberinfrastructure of the sort the ERN can offer through future funding opportunities such as the Mid-Scale RI-1 program and DMREF. As with Cryo-EM/Cryo-ET, this session will explore possibilities for extending collaborations to include other institutions as well as the community of Research Computing and Networking organizations and to form a working group to focus on future workshops and content for the Mid-Scale proposal.