Challenge
America's dependence on foreign supply chains for critical minerals and materials (CMM) threatens national security, economic competitiveness, and the deployment of technologies essential for energy independence. Domestic critical mineral production is expensive, complex, and time-consuming, in part because of the many steps to identify, extract, refine, and concentrate from complex, heterogeneous sources across critical mineral supply chains.
AI Solution
AI will revolutionize the entire critical minerals supply chain and development of alternative materials by integrating geophysical data, other fundamental science data, process optimization, cost estimation, and economic modeling into one connected system. Solving this challenge demands an AI that can reason scientifically, can understand complex structure-property relationships, and can design alternatives with different compositions. Physics-based AI offers advanced predictive capabilities to identify alternatives and understand processes underlying critical mineral availability, recovery, refinement, and replacement.
Justification
DOE's existing minerals characterization datasets (e.g., METALLIC, Critical Materials Innovation Hub), combined with DOE national laboratory expertise and DOE-supported efforts in materials science, chemistry, geosciences, biology, process engineering, and economic modeling, could enable acceleration from the years-long mineral development timelines to rapid resource assessment and production optimization. Further, use of AI could reveal new strategies to replace and/or eliminate the need for CMMs in some materials and chemical processes.
National Impact
This effort will reduce reliance on adversarial nations, expand America's mineral resource base, maximize production profitability, and strengthen supply chain resilience for technologies essential to national security and economic prosperity.
Aligned Actions from the National Laboratories
FORESIGHT: AI Enabled Supply Chain Analysis
Leading Lab: Pacific Northwest National Laboratory POC: Lionel Toba
Quantum computers have the potential to dramatically accelerate complex calculations, but inefficient data preparation remains a major bottleneck to achieving quantum advantage. To meet this national challenge, Pacific Northwest National Laboratory developed “Picasso,” an AI-enabled parallel algorithm that uses classical computing to efficiently prepare data for quantum systems. By leveraging AI-augmented graph coloring to optimize how quantum workloads are grouped, Picasso can process problems of unprecedented scale—handling millions of Pauli strings in minutes and one trillion-plus relationships while using modest GPU memory. This advance significantly expands the class of problems that can be explored with quantum algorithms, accelerating progress toward practical quantum computing.