ORNL hosted its annual Smoky Mountains Computational Sciences and Engineering Conference in person for the first time since the COVID-19 pandemic.
Madhavi Martin brings a physicist’s tools and perspective to biological and environmental research at the Department of Energy’s Oak Ridge National Laboratory, supporting advances in bioenergy, soil carbon storage and environmental monitoring, and even helping solve a murder mystery.
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.
When reading the novel Jurassic Park as a teenager, Jerry Parks found the passages about gene sequencing and supercomputers fascinating, but never imagined he might someday pursue such futuristic-sounding science.
A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.
A trio of new and improved cosmological simulation codes was unveiled in a series of presentations at the annual April Meeting of the American Physical Society in Minneapolis.
A new report published by ORNL assessed how advanced manufacturing and materials, such as 3D printing and novel component coatings, could offer solutions to modernize the existing fleet and design new approaches to hydropower.
Scientists at ORNL have confirmed that bacteria-killing viruses called bacteriophages deploy a sneaky tactic when targeting their hosts: They use a standard genetic code when invading bacteria, then switch to an alternate code at later stages of
Tomás Rush began studying the mysteries of fungi in fifth grade and spent his college intern days tromping through forests, swamps and agricultural lands searching for signs of fungal plant pathogens causing disease on host plants.
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.