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More than a year ago, ORNL computational scientists raised concerns about the accuracy of using a 2-femtosecond time step in liquid water simulations. A new study confirms and deepens those concerns, revealing even greater potential for error than previously thought.

Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications. 

Researchers at ORNL have developed an innovative new technique using carbon nanofibers to enhance binding in carbon fiber and other fiber-reinforced polymer composites – an advance likely to improve structural materials for automobiles, airplanes and other applications that require lightweight and strong materials. 

Scientists at ORNL have developed a vacuum-assisted extrusion method that reduces �鶹Ӱ�� porosity by up to 75% in large-scale 3D-printed polymer parts. This new technique addresses the critical issue of porosity in large-scale prints but also paves the way for stronger composites. 

Researchers at Oak Ridge National Laboratory have developed a modeling method that uses machine learning to accurately simulate electric grid behavior while protecting proprietary equipment details. The approach overcomes a key barrier to accurate grid modeling, helping utilities plan for future demand and prevent blackouts. 

 

ORNL researchers helped introduce college students to quantum computing for the first time during the 2025 Winter Classic Invitational, providing hands-on access to real quantum hardware and training future high-performance computing users through a unique challenge that bridged classical and quantum technologies.

During his first visit to Oak Ridge National Laboratory, Energy Secretary Chris Wright compared the urgency of the Lab’s World War II beginnings to today’s global race to lead in artificial intelligence, calling for a “Manhattan Project 2.”

Hugh O’Neill’s lifelong fascination with the complexities of the natural world drives his research at ORNL, where he’s using powerful neutron beams to dive deep into the microscopic realm of biological materials and unlock secrets for better production of domestic biofuels and bioproducts.

Researchers at Oak Ridge National Laboratory have developed a new automated testing capability for semiconductor devices, which is newly available to researchers and industry partners in the Grid Research Integration and Deployment Center.

P&G is using simulations on the ORNL Summit supercomputer to study how surfactants in cleaners cause eye irritation. By modeling the corneal epithelium, P&G aims to develop safer, concentrated cleaning products that meet performance and safety standards while supporting sustainability goals.