A collaboration between the USGS, DARPA, and ARPA-E called CriticalMAAS could deliver AI tools to solve US critical mineral challenges.
“Geologists and innovators from the U.S. Geological Survey, the Defense Advanced Research Projects Agency (DARPA), the Advanced Research Projects Agency-Energy (ARPA-E), and other partners came together Jan. 13-17 to collaborate, train, and transition artificial intelligence (AI) tools to streamline mineral resource assessment workflows.”
“The Critical Mineral Assessment with AI Support (CriticalMAAS) project set out to develop machine-learning tools to accelerate time-consuming parts of USGS data interpretation and critical mineral assessments. The collaborative five-day “hackathon” workshop served as a launch pad of progress for researchers and USGS users.”
“Graham Lederer, a USGS research geologist and lead for the collaboration with DARPA, explained that the current process for mineral resource assessments relies heavily on staff-intensive data compilation and analysis.”
“Typical mineral resource assessment will take us two years, start to finish, and that’s just for one deposit type, which may contain one mineral commodity in one area of the country,” said Lederer. “To assess 50 commodities across 100 deposit types throughout the entire United States would take many years to complete.”
“The challenge now becomes to augment and accelerate the assessment timeline from years to days.”
“Since February 2024, CriticalMAAS has conducted a dozen pilot critical mineral assessments, and the results have been promising, said Erica Briscoe, DARPA Information Innovation Office program manager. Hackathons have demonstrated the AI tools’ ability to reduce the critical mineral assessment workflow to two and half days, start to finish.”
“A hackathon held earlier in 2024 reproduced assessments covering national-scale assessments of zinc, copper, and nickel. Another hackathon that took place at the end of 2024 pivoted to conduct regional assessments of regional Mississippi Valley Type zinc, magmatic cobalt and nickel in the upper U.S. Midwest, lacustrine lithium, tungsten skarn in Alaska, national peralkaline and carbonatite rare earth elements, and regional and national porphyry copper.”
“The CriticalMAAS effort has four technical areas: extracting geospatial data from maps and documents; model extraction from knowledge; mineral potential mapping exploiting multi-modal fusion; and human -in-the-loop learning and mixed-initiative learning.”
A Geodyssey - Geoscience Text Analytics and Enterprise Search Research 
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