Quantum precision reached in modeling molten salt behavior
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.
Benjamin Manard, a nuclear analytical chemist at ORNL, has been named the 2025 winner of the Emerging Leader in Atomic Spectroscopy Award from Spectroscopy magazine.
Phong Le is a computational hydrologist at ORNL who is putting his skills in hydrology, numerical modeling, machine learning and high-performance computing to work quantifying water-related risks for humans and the environment.
Researchers at the Department of Energy’s Oak Ridge National Laboratory are using non-weather data from the nationwide weather radar network to understand how to track non-meteorological events moving through the air for better emergency response.
During Hurricanes Helene and Milton, ORNL deployed drone teams and the Mapster platform to gather and share geospatial data, aiding recovery and damage assessments. ORNL's EAGLE-I platform tracked utility outages, helping prioritize recovery efforts. Drone data will train machine learning models for faster damage detection in future disasters.
The US focuses on nuclear nonproliferation, and ORNL plays a key role in this mission. The lab conducts advanced research in uranium science, materials analysis and nuclear forensics to detect illicit nuclear activities. Using cutting-edge tools and operational systems, ORNL supports global efforts to reduce nuclear threats by uncovering the history of nuclear materials and providing solutions for uranium removal.
A team of researchers at ORNL are using virtual reality to understand normal and abnormal human behavior in a given location – specifically, a nuclear reactor. As people move around their lives, they tend to do similar activities in the same
ORNL researchers created and tested two methods for transforming coal into the scarce mineral graphite, which is used in batteries for electric vehicles.
Researchers are using machine learning to provide a more complete picture of building geometries that include building height to within three meters of accuracy. This model not only provides building height for any building in the world, but it will also feed into LandScan and other large government datasets for planning and response.
Scientists and land managers interested in accessing the first dataset of its kind on one of the most biologically diverse ecosystems in the world were given hands-on tutorials during a recent workshop by researchers supporting the ORNL Distributed Active Archive Center for Biogeochemical Dynamics.