Synthèse
While we have a lot of work still to do, the practical impact of the Genesis Mission is already coming to life across the laboratory ecosystem. At Pacific Northwest National Laboratory (PNNL), senior scientist Dr. Henry Kvinge is using AlphaEvolve to map out massive mathematical systems that are far too complex for humans to explore by hand. The AI uncovers hidden connections automatically, fast-tracking discoveries that would normally take researchers years to find. “Modern math relies on abstr
While we have a lot of work still to do, the practical impact of the Genesis Mission is already coming to life across the laboratory ecosystem. At Pacific Northwest National Laboratory (PNNL), senior scientist Dr. Henry Kvinge is using AlphaEvolve to map out massive mathematical systems that are far too complex for humans to explore by hand. The AI uncovers hidden connections automatically, fast-tracking discoveries that would normally take researchers years to find. “Modern math relies on abstraction, but combinatorics offers concrete models that make complex geometry and algebra easier to grasp.
We’ve found that systems like AlphaEvolve are perfect for this search,” said Dr. Kvinge. “By leveraging the broad mathematical knowledge of LLMs, we can automate the exploration of countless angles. We’re still experimenting, but the discoveries are already shaping our future research. ” At the National Laboratory of the Rockies (NLR), researchers are utilizing Gemini to fundamentally change how they interact with physical laboratory hardware.
Dr. Steven R. Spurgeon, a senior materials data scientist at NLR, leads a pioneering program in autonomous materials discovery. "Our collaboration has allowed us to build an autonomous experimentation capability," said Dr. Spurgeon.
"By deploying Gemini in our instruments, we cut microscope calibration time from over 90 minutes to about 13 minutes (eight times faster) and reduced the manual steps needed to focus an image from as many as 50 down to two. That's time and attention we've given back to the science itself, enabling genuinely autonomous workflows that observe, reason, and decide in real time. This has helped us explore parts of the material design space we simply could not have reached through manual operation alone.
Source originale
pnnl.gov