Past midnight in the Hearst Memorial Mining Building on the campus of the University of California, Berkeley, beyond a vaulted entrance and down a marble staircase, the experiments in the A-Lab are running without people. Powdered precursors and oxides twirl through the laboratory in crucibles shaped like sake cups, then are slurried and spun in centrifuges with zirconium beads, baked in industrial ovens, scanned using x-ray diffraction and, in battery tests, measured for ionic conductivity. Each result feeds the next experiment.
When something goes wrong—a jammed rack, a spilled sample, a precursor running out—the choreography halts. Minerva, Alfred, Prometheus, Jeeves, and a handful of other artificial intelligence–enabled robots that run the lab overnight can’t always reset it themselves. A sleeping graduate student gets an e-mail and a Slack alert, then can log in from bed, check the lab’s cameras and try to fix the problem.
“We want a better material,” says Gerbrand Ceder, the U.C. Berkeley materials scientist who runs the A-Lab. “But we’re also really interested in: Can you build an AI that acts like a scientist?”
The A-Lab is one of a few sites where a new research infrastructure is taking shape. Operated by U.C. Berkeley and Lawrence Berkeley National Laboratory (LBNL), it pairs robotics and lab automation with a custom AI agent that interprets results and proposes the next round of experiments, backed by LBNL’s computing resources. Researchers call it a lab in the loop: a system that can experiment, iterate and suggest the next step. “I don’t think people know what’s about to hit them,” Ceder says.
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