The University of South Dakota will host the inaugural Workshop for Artificial Intelligence (AI)-Powered Materials Discovery in the Great Plains. This workshop will leverage the unique geographical strengths of the Great Plains to bring together approximately 200 researchers and educators from nine EPSCoR jurisdictions, along with 40 leading experts in AI, engineering, materials science, physical science, data science, and education from around the world.
The primary goal is to create opportunities for these jurisdictions to play pivotal roles in developing a world-class, data-driven materials research and education platform. This platform will emphasize AI-driven approaches for designing and producing high-performance materials and advancing education in these fields. The envisioned platform will harness machine learning (ML) to innovate the dynamic characterization, design, and production of functional materials. The core hypothesis is that data-driven ML algorithms can significantly accelerate materials discovery by accurately predicting material structures and functionalities while extracting critical insights from diverse data inputs. The workshop aims to gather a broad range of global expertise to establish a consortium dedicated to advancing AI-driven technologies and education, thereby accelerating the pace of materials discovery.
The University of South Dakota is now accepting contributed talks for the Workshop for AI-Powered Materials Discovery in the Great Plains. This workshop will bring together researchers and educators to explore AI-driven approaches for materials research and education. Each contributed talk should be 20 minutes long, followed by 4 minutes for questions. We invite researchers from various disciplines, including AI, engineering, materials science, physical sciences, and data science, to submit their proposals and share their insights on advancing AI-powered materials discovery.
Additionally, participants submitting abstracts for the workshop should ensure that their abstracts do not exceed 200 words. Abstracts should focus on innovative research, methodologies, or applications related to AI-driven materials discovery and education.
Conference Chair: Dr. Dongming Mei
