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New system combines human, artificial intelligence to improve experimentation

To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance. 

: ORNL climate modeling expertise contributed to an AI-backed model that assesses global emissions of ammonia from croplands now and in a warmer future, while identifying mitigation strategies. This map highlights croplands around the world. Credit: U.S. Geological Survey

ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.

Using a better modeling framework, with data collected from Mississippi Delta marshes, scientists are able to improve the predictions of methane and other greenhouse gas emissions. Credit: Matthew Berens/ORNL, U.S Dept. of Energy

Scientists at the Department of Energy’s Oak Ridge National Laboratory are using a new modeling framework in conjunction with data collected from marshes in the Mississippi Delta to improve predictions of climate-warming methane and nitrous oxide.

ORNL Associate Laboratory Director for Computing and Computational Sciences. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Gina Tourassi, associate laboratory director for computing and computational sciences at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory, has been named a fellow of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals.

Caption: Jaswinder Sharma makes battery coin cells with a lightweight current collector made of thin layers of aligned carbon fibers in a polymer with carbon nanotubes. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Electric vehicles can drive longer distances if their lithium-ion batteries deliver more energy in a lighter package. A prime weight-loss candidate is the current collector, a component that often adds 10% to the weight of a battery cell without contributing energy.

The Department of Energy’s latest Fuel Economy Guide includes 2024 model vehicle fuel efficiency data compiled by ORNL researchers, as well as a tool for mapping the most economical driving route. Credit: ORNL/U.S. Dept. of Energy

Oak Ridge National Laboratory researchers have identified the most energy-efficient 2024 model year vehicles available in the United States, including electric and hybrids, in the latest edition of the Department of Energy’s Fuel Economy Guide.

Researchers used Frontier, the world’s first exascale supercomputer, to simulate a magnesium system of nearly 75,000 atoms and the National Energy Research Computing Center’s Perlmutter supercomputer to simulate a quasicrystal structure, above, in a ytterbium-cadmium alloy. Credit: Vikram Gavini

Researchers used the world’s first exascale supercomputer to run one of the largest simulations of an alloy ever and achieve near-quantum accuracy.

Hilda Klasky

Hilda Klasky, an R&D staff member in the Scalable Biomedical Modeling group at ORNL, has been selected as a senior member of the Association of Computing Machinery, or ACM.

SM2ART team members receive the CAMX Combined Strength Award at the Georgia World Congress Center in Atlanta. Pictured here are, from left, ORNL’s Dan Coughlin, Sana Elyas, Halil Tekinalp, Amber Hubbard, Soydan Ozcan; University of Maine’s Susan MacKay, Angelina Buzzelli, Scott Tomlinson, Wesley Bisson; and ORNL’s Matt Korey and Vlastimil Kunc. Credit: University of Maine

The Hub & Spoke Sustainable Materials & Manufacturing Alliance for Renewable Technologies, or SM2ART, program has been honored with the composites industry’s Combined Strength Award at the Composites and Advanced Materials Expo, or CAMX, 2023 in Atlanta. This distinction goes to the team that applies their knowledge, resources and talent to solve a problem by making the best use of composites materials.

Oak Ridge National Laboratory researchers took a connected and automated vehicle out of the virtual proving ground and onto a public road to determine energy savings when it is operated under predictive control strategies. Credit: ORNL, U.S. Dept. of Energy

ORNL researchers  determined that a connected and automated vehicle, or CAV, traveling on a multilane highway with integrated traffic light timing control can maximize energy efficiency and achieve up to 27% savings.