A team of students led by researchers in the Duke University Critical Minerals Hub will use data-driven methods to develop machine learning models that predict critical mineral presence and abundance in mine waste and acid mine drainage. Students will integrate large geological and geochemical datasets, identify key indicators of critical mineral presence in mine waste solids and water chemistry, and apply machine learning techniques to predict critical mineral abundance across diverse sites. Students will learn about data science methods, including model development, validation, interpretation, and visualization, in an interdisciplinary project that includes mining, environmental policy, and geochemical processes. This work will be foundational for the development of a decision-making tool to help guide researchers, policy makers, and industry professionals in the sustainable recovery of critical minerals needed to advance the clean energy transition.
Project Leads: Dr. Leanne Gilbertson, Dr. Heileen Hsu-Kim, and Dr. Clara Park
Project Manager: Patrick Dunn
