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
National Challenge: Securing America’s Critical Minerals Supply
Leading Lab: Idaho National Laboratory POC: Lionel TobaGlobalized trade has dramatically increased supply chain complexity, limiting the effectiveness of traditional analytics and slowing the nation’s ability to anticipate disruptions in critical materials. To address this national challenge, the Idaho National Laboratory is leading the development of FORESIGHT, an AI-enabled supply chain analysis capability. It applies big data, machine learning and advanced reasoning to identify emerging risks and predict potential disruptions across critical material value chains. By uncovering correlations, dependencies and causal relationships among supply chain events and entities, FORESIGHT enables rapid, scenario-based analysis that can be updated continuously as new information becomes available. This AI-driven approach reduces analysis timelines from months to days, providing policymakers, defense stakeholders and industry leaders with timely, actionable insights to proactively manage risks and strengthen U.S. supply chain resilience.
Close-up structure of the terbium-binding site in an AI-discovered protein. This structure connects AI-guided biomolecule discovery with rare-earth terbium capture, supporting a cleaner domestic supply of critical minerals.National Challenge: Securing America’s Critical Minerals Supply
Leading Lab: Brookhaven National LaboratoryExtracting a critical mineral from ore or waste can take more than 500 chemical steps, using toxic and expensive reagents at high temperatures. Brookhaven National Laboratory is replacing that chemistry with biology, using AI-engineered biomolecules and cells that bind specific elements at ambient conditions.
By leveraging AI and closed-loop autonomous laboratories, this approach could compress 500 steps to fewer than five at equal or higher purity. AI already has helped to discover two biomolecules that capture a critical rare earth element, terbium. The payoff is a domestic critical minerals supply that is faster, cheaper, and far less chemically intensive to produce.
Elias Nakouzi, a materials scientist at Pacific Northwest National Laboratory, leads research using artificial intelligence and automated experimentation to accelerate the recovery of critical materials from complex feedstocks.National Challenge: Securing America’s Critical Minerals Supply
Leading Lab: Pacific Northwest National LaboratoryRecovering critical materials from complex waste streams and other feedstocks can be difficult and time-consuming, limiting the ability to develop new domestic sources of materials needed for energy technologies.
Pacific Northwest National Laboratory (PNNL) is developing an agentic AI-guided workflow that autonomously plans, evaluates, and improves selective precipitation processes for critical materials recovery. Built on PNNL’s SciLink multi-agent automation platform, the closed-loop system uses AI to learn from experimental results and identify more effective separation conditions.
The approach has recovered high-purity magnesium from produced water, samarium from samarium-cobalt magnets, and rare earth elements from neodymium-iron-boron magnets with high yields. By automating and accelerating process development, the workflow can shorten the search for efficient, adaptable, and scalable separation methods from months or years to days.