Savannah River National Laboratory researcher outlines AI plan to scale lab results for DOE projects

Nathan Morgan, a researcher at Savannah River National Laboratory, outlined an effort to apply advanced artificial intelligence to the long-standing challenge of converting laboratory-scale results into full-scale industrial systems. He said the work is part of the Genesis Mission Project and described an "AI-powered scale up engine" intended for Department of Energy uses.

"What we're trying to do is sidestep all of this effort and make a AI powered scale up engine that's suitable for Department of Energy projects," Morgan said. He framed the approach as a way to avoid the costly, resource-intensive process of full-scale testing that often follows laboratory work, adding that the effort aims to "eliminate the need for costly expensive tests" and thereby "save time and money."

Morgan identified the core problem as the technical and financial difficulty of translating small-scale laboratory results to industrial systems, saying the process "generates its own mess." He described the proposed AI tool as a way to model or otherwise substitute for some physical scale-up tests to reduce that burden, though he did not provide technical details or timelines in the remarks.

The presentation focused on the concept and intended application rather than implementation specifics. Morgan did not offer cost estimates, a deployment schedule, or details about validation methods during these remarks; those items were not specified in the provided transcript.