Scientists at ORNL used their knowledge of complex ecosystem processes, energy systems, human dynamics, computational science and Earth-scale modeling to inform the nation’s latest National Climate Assessment, which draws attention to vulnerabilities and resilience opportunities in every region of the country.
In fiscal year 2023 — Oct. 1–Sept. 30, 2023 — Oak Ridge National Laboratory was awarded more than $8 million in technology maturation funding through the Department of Energy’s Technology Commercialization Fund, or TCF.
ORNL hosted its annual Smoky Mountains Computational Sciences and Engineering Conference in person for the first time since the COVID-19 pandemic.
Wildfires have shaped the environment for millennia, but they are increasing in frequency, range and intensity in response to a hotter climate. The phenomenon is being incorporated into high-resolution simulations of the Earth’s climate by scientists at the Department of Energy’s Oak Ridge National Laboratory, with a mission to better understand and predict environmental change.
ORNL will team up with six of eight companies that are advancing designs and research and development for fusion power plants with the mission to achieve a pilot-scale demonstration of fusion within a decade.
A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.
Researchers at ORNL are tackling a global water challenge with a unique material designed to target not one, but two toxic, heavy metal pollutants for simultaneous removal.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.