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Media Contacts
![New system combines human, artificial intelligence to improve experimentation](/sites/default/files/styles/list_page_thumbnail/public/2024-02/Screenshot%202024-02-14%20at%2011.37.46%20AM%20%281%29.png?h=e621a1e2&itok=N3lsBqrh)
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](/sites/default/files/styles/list_page_thumbnail/public/2024-02/global_croplands_usgs_globe-4g_1.png?h=4016a495&itok=rb8eHyvK)
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](/sites/default/files/styles/list_page_thumbnail/public/2024-01/picture1_0.jpg?itok=uQVJerN0)
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](/sites/default/files/styles/list_page_thumbnail/public/2024-01/tourassi.jpg?h=55be468c&itok=AvEfpuPK)
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.
![Researchers at Corning have found that understanding the stability of the rings of atoms in glass materials can help predict the performance of glass products.](/sites/default/files/styles/list_page_thumbnail/public/2024-01/picture2.png?h=342db57d&itok=yUdVp1Za)
Corning uses neutron scattering to study the stability of different types of glass. Recently, researchers for the company have found that understanding the stability of the rings of atoms in glass materials can help predict the performance of glass products.
A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%.
![From left, Cable-Dunlap, Chi, Smith and Thornton have been named ORNL Corporate Fellows. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/corpfellow_nov23_2.jpg?h=d1cb525d&itok=G_PduE-d)
Four researchers at the Department of Energy’s Oak Ridge National Laboratory have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
![Gina Tourassi. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/2023-P18395%5B30%5D_1.jpg?h=8f9cfe54&itok=pTv9bdLA)
Effective Dec. 4, Gina Tourassi will assume responsibilities as associate laboratory director for the Computing and Computational Sciences Directorate at the Department of Energy’s Oak Ridge National Laboratory.
![2023 Battelle Distinguished Inventors](/sites/default/files/styles/list_page_thumbnail/public/2023-11/23-G07641-Battelle-Distinguished-Inventor-graphic-pcg_0.jpg?h=d1cb525d&itok=uhmqAKgT)
Four scientists affiliated with ORNL were named Battelle Distinguished Inventors during the lab’s annual Innovation Awards on Dec. 1 in recognition of being granted 14 or more United States patents.
![Conceptual art depicts machine learning finding an ideal material for capacitive energy storage. Its carbon framework (black) has functional groups with oxygen (pink) and nitrogen (turquoise). Credit: Tao Wang/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-11/Press%20release%20image_0.jpg?h=706c9a24&itok=zX1lC5ud)
Guided by machine learning, chemists at ORNL designed a record-setting carbonaceous supercapacitor material that stores four times more energy than the best commercial material.