“A research team at the Department of Energy’s Pacific Northwest National Laboratory has deployed AI agents with the potential to accelerate the recovery of critical minerals from real-world industrial waste in days instead of the months or years required for manual experimentation…
To demonstrate the value of the system, the research team tested three different industrial wastes: two different kinds of spent magnets and wastewater from oil and gas extraction.
The scientists fed a description of what was in the waste to specially designed AI agents. The agents then evaluated the value, concentration, and potential product purity after a separation procedure, before making a technical and economic recovery recommendation. In the trial runs, the AI agents recommended recovery of the element magnesium from wastewater produced during oil and gas extraction, of neodymium and praseodymium from magnet waste, and of samarium, a rare-earth element critical to high-performance aerospace magnets and nuclear reactors.
Such feedstock evaluations traditionally take months of analysis and preliminary lab protocol preparation.
Instead, within a day, the AI agents used published scientific literature to develop a plan for 96 simultaneous experiments, including recipes for all chemicals used for separation, their order of addition, and timing steps. A liquid-handling robot then executed the orders.
For these initial experiments, human operators prepared the completed experimental samples for final chemical analysis. But the resulting data were automatically evaluated by AI for any necessary refinements, and if needed, a second round of 96 experiments to optimize purity and yield.”
