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Media Contacts
![From left, researchers Syed Islam and Ramesh Bhave discuss the nickel sulfate recovered from end-of-life lithium-ion batteries using the membrane solvent extraction process they co-invented at ORNL. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/Main-image-Islam-and-Bhave-2023-P17689.jpg?h=aa34054d&itok=B0luDpWK)
Scientists at ORNL have developed a technique for recovering and recycling critical materials that has garnered special recognition from a peer-reviewed materials journal and received a new phase of funding for research and development.
![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.
![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.
![Image of circuitry representing AI.](/sites/default/files/styles/list_page_thumbnail/public/2023-11/ai-generic_0.jpg?h=7a6e80fd&itok=kM92w4I_)
Research performed by a team, including scientists from ORNL and Argonne National Laboratory, has resulted in a Best Paper Award at the 19th IEEE International Conference on eScience.
![Frontier’s exascale power enables the Simple Cloud-Resolving E3SM Atmosphere Model to run years’ worth of climate simulations at unprecedented speed and scale. Credit: Ben Hillman/Sandia National Laboratories, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-12/E3SM-MMF.png?h=21f5ce54&itok=dsj1Hwvc)
A 19-member team of scientists from across the national laboratory complex won the Association for Computing Machinery’s 2023 Gordon Bell Special Prize for Climate Modeling for developing a model that uses the world’s first exascale supercomputer to simulate decades’ worth of cloud formations.
![A Univ. of Michigan-led team used Frontier, the world’s first exascale supercomputer, to simulate a system of nearly 75,000 magnesium atoms at near-quantum accuracy. Credit: SC23](/sites/default/files/styles/list_page_thumbnail/public/2023-12/Gavini-SC23_1116_awards-20.jpg?h=c6980913&itok=LQLYh4jz)
A team of eight scientists won the Association for Computing Machinery’s 2023 Gordon Bell Prize for their study that used the world’s first exascale supercomputer to run one of the largest simulations of an alloy ever and achieve near-quantum accuracy.
![Sangkeun “Matt” Lee received the Best Poster Award at the Institute of Electrical and Electronics Engineers 24th International Conference on Information Reuse and Integration.](/sites/default/files/styles/list_page_thumbnail/public/2023-12/MattLee.jpg?h=4a7d1ed4&itok=V-iscVnI)
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.
![A small droplet of water is suspended in midair via an electrostatic levitator that lifts charged particles using an electric field that counteracts gravity. Credit: Iowa State University/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-11/droplet.png?h=ddb1ad0c&itok=3nblnUcm)
How do you get water to float in midair? With a WAND2, of course. But it’s hardly magic. In fact, it’s a scientific device used by scientists to study matter.
![From left are Analytics and AI Methods at Scale group leader Feiyi Wang, technical lead Mike Matheson and research scientist Hao Lu.](/sites/default/files/styles/list_page_thumbnail/public/2023-11/2023-P12429_0.jpg?h=55be468c&itok=tajHF4hU)
The team that built Frontier set out to break the exascale barrier, but the supercomputer’s record-breaking didn’t stop there.