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In this artist’s conception of the process, an automated deposition system places a new material onto a base material (purple beam, right) as the last sample that was made is analyzed and sent to the AI (green beams, brain, left). The AI tells the pulsed laser deposition machine what to do next (data cable, bottom).
Researchers harness AI for autonomous discovery and optimization of materials
Students gather at a poster session
Students acquire skills at Quantum Science Center
Rectangular box being lifted by a red pully system up the left side of the building
Researchers make ‘green’ floor to replace steel

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Jason Gardner, Sandra Davern and Peter Thornton have been elected fellows of AAAS. Credit: Laddy Fields/ORNL, U.S. Dept. of Energy

AAAS elects three ORNL scientists as fellows

Three scientists from the Department of Energy’s Oak Ridge National Laboratory have been elected fellows of the American Association for the Advancement of Science, or AAAS.

An algorithm developed and field-tested by ORNL researchers uses machine learning to maintain homeowners’ preferred temperatures year-round while minimizing energy costs. Credit: ORNL, U.S. Dept. of Energy

Energy – Building a better thermostat

Oak Ridge National Laboratory researchers designed and field-tested an algorithm that could help homeowners maintain comfortable temperatures year-round while minimizing utility costs.

Heat impact map

Climate—Energy demand shuffle

A detailed study by Oak Ridge National Laboratory estimated how much more—or less—energy United States residents might consume by 2050 relative to predicted shifts in seasonal weather patterns