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Neus Domingo Marimon, leader of the Functional Atomic Force Microscopy group at the Center for Nanophase Materials Sciences of ORNL, has been elevated to senior member of the Institute of Electrical and Electronics Engineers.

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.

Chad Parish, a senior researcher at ORNL, studies materials at the atomic level to improve nuclear reactors. His work focuses on fusion and fission energy, using microscopy and collaborating with experts to advance materials for extreme environments.

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. 

The ForWarn visualization tool was co-developed by ORNL with the U.S. Forest Service. The tool captures and analyzes satellite imagery to track impacts such as storms, wildfire and pests on forests across the nation.

Researchers at 91���� used the Frontier supercomputer to train the world’s largest AI model for weather prediction, paving the way for hyperlocal, ultra-accurate forecasts. This achievement earned them a finalist nomination for the prestigious Gordon Bell Prize for Climate Modeling.

Researchers set a new benchmark for future experiments making materials in space rather than for space. They discovered that many kinds of glass have similar atomic structure and arrangements and can successfully be made in space. Scientists from nine institutions in government, academia and industry participated in this 5-year study. 

ORNL researchers modeled how hurricane cloud cover would affect solar energy generation as a storm followed 10 possible trajectories over the Caribbean and Southern U.S.

New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses. 
 

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.