Travel Support for Workshop on Best Practices in Modeling Across Scales from Materials Discovery to Manufacturing; Arlington, Virginia; Summer 2023

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

This award provides travel support for key speakers and presenters to attend the workshop on “Best Practices in Modeling Across Scales from Materials Discovery to Manufacturing: Towards Systematic Analysis of Errors and Uncertainty." This workshop will address the need to understand the challenges and opportunities in the area of first principles modeling for materials innovation and manufacturing. The workshop provides a forum to share leading-edge research on materials modeling, uncertainty quantification and error analysis. It offers participants an opportunity to showcase their scientific accomplishments, interact with peers and colleagues from academia, government labs, and industry, extend their network, and foster new collaborations within the broader advanced materials and manufacturing communities. The workshop provides attendees with a broader view of the materials discovery, design, and manufacturing cycle, that are critical to advancing new technologies that benefit society. Consistent with NSF's goal of broadening participation in science and engineering, the workshop seeks participation of women and underrepresented minority groups as speakers and attendees. The workshop meets the national priority of fostering advanced materials and manufacturing. This workshop provides a platform for experts to present and discuss their research and results in Best Practices in Systematic Analysis of Errors and Uncertainty Across Scales from Materials Modeling and Discovery to Manufacturing. The workshop participants will discuss the application of Data Science, Machine Learning and Artificial Intelligence (AI) in developing First Principles Modeling of Materials and their Manufacturing. The workshop brings together researchers and experts in modeling and simulation, multiscale modeling, machine learning, informatics, data analytics, artificial intelligence for process design and manufacturing to discuss cross-cutting pathways, challenges, and opportunities. The goal is to determine and integrate error and uncertainty analysis into standard computational workflows, so that rigorous error determination and reporting become a standard norm for the materials modeling community. The workshop focuses on innovative pathways for new materials discovery, their design and manufacture, and identification of cross-cutting research opportunities. The workshop is organized into different sessions and includes talks by leading domestic and international experts discussing their latest research and identifying research gaps on topics such as (1) Error control for electronic structure, first principles, and quantum chemistry methods, (2) Best practices for modelers when working with experimentalists, (3) Error control at higher scales: interatomic potentials including machine learned error propagation in multiscale materials modeling, (4) Error control for materials synthesis, processing, and manufacture, (5) Incorporating error analysis into workflows and materials data repositories. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.