Quantum precision reached in modeling molten salt behavior
Strengthening the competitiveness of the U.S. transportation industry depends on developing domestic EV batteries that combine rapid charging with long-range performance — two goals that often conflict. Researchers at ORNL have addressed this challenge by redesigning a key battery component, enabling fast, 10-minute charging while improving energy density and reducing reliance on copper.
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 a tool that gives builders a quick way to measure, correct and certify level foundations. FLAT, or the Flat and Level Analysis Tool, examines a 360-degree laser scan of a construction site using ORNL-developed segmentation algorithms and machine learning to locate uneven areas on a concrete slab.
Scientists at ORNL have developed a method that can track chemical changes in molten salt in real time — helping to pave the way for the deployment of molten salt reactors for energy production.
The Department of Energy announced a $67 million investment in several AI projects from institutions in both government and academia as part of its AI for Science initiative. Six ORNL-led (or co-led) projects received funding.
A study led by the Department of Energy’s Oak Ridge National Laboratory details how artificial intelligence researchers created an AI model to help identify new alloys used as shielding for housing fusion applications components in a nuclear reactor. The findings mark a major step towards improving nuclear fusion facilities.
In a game-changing study, ORNL scientists developed a deep learning model — a type of artificial intelligence that mimics human brain function — to analyze high-speed videos of plasma plumes during a process called pulsed laser deposition.
ORNL has partnered with Western Michigan University to advance intelligent road infrastructure through the development of new chip-enabled raised pavement markers. These innovative markers transmit lane-keeping information to passing vehicles, enhancing safety and enabling smarter driving in all weather conditions.
A team led by scientists at ORNL identified and demonstrated a method to process a plant-based material called nanocellulose that reduced energy needs by a whopping 21%, using simulations on the lab’s supercomputers and follow-on analysis.
DOE commissioned a neutron imaging instrument, VENUS, at the Spallation Neutron Source in July. VENUS instrument scientists will use AI to deliver 3D models to researchers in half the time it typically takes.