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Media Contacts
![2023 Top Science Achievements at SNS & HFIR](/sites/default/files/styles/list_page_thumbnail/public/2023-12/23-G08001-SNS-Top-Story-Image-pcg.jpg?h=1f0bc3a8&itok=3_ZyuAAO)
The 2023 top science achievements from HFIR and SNS feature a broad range of materials research published in high impact journals such as Nature and Advanced Materials.
![Alexey Serov researches ways to improve hydrogen fuel cells and materials and the electrolysis process. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/2023-P14038.jpg?h=c6980913&itok=JCWAG0sI)
It would be a challenge for any scientist to match Alexey Serov’s rate of inventions related to green hydrogen fuel. But this researcher at ORNL has 84 patents with at least 35 more under review, so his electrifying pace is unlikely to slow down any time soon.
![ORNL scientist Zhijia Du, white coat, former ORNL scientist Jianlin Li, blue coat, and Ateios CEO Rajan Kumar inspect battery components during a pilot production run. Credit: Kurt Weiss/ORNL, U.S. Dept of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/Kumar.2023-P12479.jpg?h=22d8bc0e&itok=SexbmLnB)
Ateios Systems licensed an ORNL technology for solvent-free battery component production using electron curing. Through Innovation Crossroads, Ateios continues to work with ORNL to enable readiness for production-quality battery components.
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%.
![Photo by James Wainscoat on Unsplash.](/sites/default/files/styles/list_page_thumbnail/public/2023-12/SWARM%203.png?h=fa0a1eed&itok=Yehe18le)
A team of researchers from the University of Southern California, the Renaissance Computing Institute at the University of North Carolina, and Oak Ridge, Lawrence Berkeley and Argonne National Laboratories have received a grant from the U.S. Department of Energy to develop the fundamentals of a computational platform that is fault tolerant, robust to various environmental conditions and adaptive to workloads and resource availability.
![A researcher plays checkers against an AI-powered robotic arm in 1984. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/AI%201.jpg?h=a126eea8&itok=AjOX9bCw)
Despite its futuristic essence, artificial intelligence has a history that can be traced through several decades, and the ORNL has played a major role. From helping to drive fundamental and applied AI research from the field’s early days focused on expert systems, computer programs that rely on AI, to more recent developments in deep learning, a form of AI that enables machines to make evidence-based decisions, the lab’s AI research spans the spectrum.
![Alex May, pictured above, is the first and only full-time data curator at the Department of Energy’s Oak Ridge Leadership Computing Facility. Credit: Carlos Jones and Wikimedia Commons, background/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/2023-P18433%20%281%29_0.jpg?h=8f9cfe54&itok=DQKdmnrN)
![QSC Director Travis Humble, who gave a lunchtime talk on transitioning good ideas to device development, learns about one of the many quantum research efforts featured at the poster session. Credit: Alonda Hines/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/2023-P16851.jpg?h=036a71b7&itok=7MltSEYq)
On Nov. 1, about 250 employees at Oak Ridge National Laboratory gathered in person and online for Quantum on the Quad, an event designed to collect input for a quantum roadmap currently in development. This document will guide the laboratory's efforts in quantum science and technology, including strategies for expanding its expertise to all facets of the field.
![The AI agent, incorporating a language model-based molecular generator and a graph neural network-based molecular property predictor, processes a set of user-provided molecules (green) and produces/suggests new molecules (red) with desired chemical/physical properties (i.e. excitation energy). Image credit: Pilsun You, Jason Smith/ORNL, U.S. DOE](/sites/default/files/styles/list_page_thumbnail/public/2023-12/image001_0.png?h=16ec4b77&itok=KtCjteSq)
A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules.
![ORNL researchers contributed biomass resources analysis to a new report that says carbon dioxide removal targets can be reached by 2050 using existing technology. Source: Jason Richards/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/Picture4_0.jpg?h=46e9bf6f&itok=Rvklgpoj)
Scientists from more than a dozen institutions have completed a first-of-its-kind high-resolution assessment of carbon dioxide removal potential in the United States, charting a path to achieve a net-zero greenhouse gas economy by 2050.