Genesis Mission deploys AI agents to recover critical metals from lithium-ion battery waste

SLAC leads multi-agent AI system to tackle battery waste challenge

Researchers at SLAC National Accelerator Laboratory are building a multi-agent AI system designed to recover critical metals from lithium-ion battery waste, targeting materials that power electric vehicles and modern energy technologies1

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. The project, developed in collaboration with the University of Southern California, focuses on extracting cobalt, nickel, manganese and other valuable transition metals from spent batteries that have reached the end of their useful life2

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The initiative addresses a pressing national challenge: a single spent electric vehicle battery contains tens of pounds of valuable metals, yet current extraction methods require large amounts of chemicals, generate substantial waste, and involve extensive trial and error2

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. By assembling specialized AI agents with distinct roles, the team aims to explore chemical pathways across diverse fields ranging from biochemistry to geology, proposing strategies for metal separation and learning from experimental results1

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Genesis Mission drives integrated scientific discovery platform

This battery recycling project represents one component of the US Department of Energy's broader Genesis Mission, a national initiative building an integrated science discovery platform that combines AI, supercomputing, quantum systems and advanced instruments to accelerate scientific discovery1

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. The funding awards announced at the Genesis Mission Summit in Washington, D.C., mark the first phase of investments designed to identify promising research pathways and establish foundations for future scale2

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SLAC will participate in 10 additional Genesis Mission Phase I projects spanning biotechnology, fusion energy, electronics and sensors, cosmology, particle physics, and accelerator technology1

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. "SLAC's role as a leading collaborator across a range of technologies positions us to advance AI's transformative role in scientific discovery," said SLAC Lab Director John Sarrao1

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Target: 80% purity in recovered metals over nine months

The SLAC-led team will run several AI-experiment cycles over nine months, aiming to recover critical metals at 80% purity or better while benchmarking this approach against conventional literature search and recovery methods1

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. "Over nine months, SLAC and the University of Southern California will run several AI-experiment cycles, aiming to recover target metals at 80 percent purity or better," explained Ahamed Irshad Maniyanganam, SLAC associate scientist and lead researcher on the project2

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Instead of relying on one system to handle the entire research process, specialized AI agents will work on different parts of the problem and contribute to a shared experimental workflow1

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. This approach is intended to help researchers explore a much wider range of potential chemical pathways than conventional methods allow, potentially reducing the extensive trial and error currently required to identify suitable extraction and separation processes1

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Implications for domestic supply chains and energy technologies

Recovering these materials from used batteries could help create a domestic supply while reducing dependence on imported critical minerals1

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. The project addresses a key national science and technology challenge identified by the DOE earlier this year, focusing on securing the nation's critical mineral supply2

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Cobalt, nickel, and manganese are considered important materials for modern energy technologies, and recovering them from used products could provide an additional source of these resources1

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. The project's success could have implications beyond battery recycling, potentially establishing new workflows that integrate AI with scientific investigation to accelerate discovery and improve predictive capabilities across multiple domains2

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. Coinvestigators include Frank Abild-Pedersen, SLAC senior scientist, Jagjit Nanda, executive director of the SLAC-Stanford Battery Center, and Jayakanth Ravichandran, professor at the USC Viterbi School of Engineering2

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