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ORNL’s Nagi Rao discusses the lab’s deployed “dark fiber” testbed for quantum networking at SC23 in Denver, Colorado. Credit: Mariam Kiran/ORNL, U.S. Dept. of Energy

ORNL’s successes in QIS and its forward-looking strategy were recently recognized in the form of three funding awards that will help ensure the laboratory remains a leader in advancing quantum computers and networks.

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.

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.

Prasanna Balaprakash, who leads ORNL’s AI Initiative, participated in events hosted by the White House Office of Science and Technology Policy and the Task Force on American Innovation to discuss the challenges and opportunities posed by AI. Credit: Brian Mosley/Computing Research Association

In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the U.S. National Science Foundation.

2023 Top Science Achievements at SNS & HFIR

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.

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

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.

Rigoberto “Gobet” Advincula, a scientist at the Department of Energy’s Oak Ridge National Laboratory, has been named a 2023 Fellow of the National Academy of Inventors, or NAI.

Rigoberto “Gobet” Advincula, a scientist at the Department of Energy’s Oak Ridge National Laboratory, has been named a 2023 Fellow of the National Academy of Inventors. Advincula has been recognized for his 14 patents and 21 published filings related to nanomaterials, smart coatings and films, solid-state device fabrication and chemical additives.

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

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. 

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
Alex May is the first and only full-time data curator at the Department of Energy’s Oak Ridge Leadership Computing Facility, evaluating datasets developed by computational scientists before they are made public through the OLCF’s Constellation portal for open data exchange.
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

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

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.