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This illustration demonstrates how atomic configurations with an equiatomic concentration of niobium (Nb), tantalum (Ta) and vanadium (V) can become disordered. The AI model helps researchers identify potential atomic configurations that can be used as shielding for housing fusion applications in a nuclear reactor. Credit: Massimiliano Lupo Pasini/ORNL, U.S. Dept. of Energy

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.

Four thermometers are pictured across the top of the image with an image of a city in the bottom left, with a color block version of that city in the bottom right.

Researchers at Oak Ridge National Laboratory have developed free data sets to estimate how much energy any building in the contiguous U.S. will use in 2100. These data sets provide planners a way to anticipate future energy needs as the climate changes.

Red background fading into black from top to bottom. Over top the background are 20 individual rectangles lined up in three rows horizontally with a red and blue line moving through it.

ORNL scientists develop a sample holder that tumbles powdered photochemical materials within a neutron beamline exposing more of the material to light for increased photo-activation and better photochemistry data capture.

Sarah Walters portrait

Walters is working with a team of geographers, linguists, economists, data scientists and software engineers to apply cultural knowledge and patterns to open-source data in an effort to document and report patterns of human movement through previously unstudied spaces.

ORNL’s Jason DeGraw, a mechanical engineer and indoor air quality expert, uses numerical equations powered by high-performance computing to analyze and solve problems related to the dispersion patterns of biological pathogens as well as chemical irritants in buildings. Credit: ORNL, U.S. Dept. of Energy

Long before COVID-19’s rapid transmission led to a worldwide pandemic, Oak Ridge National Laboratory’s Jason DeGraw was performing computer modeling to better understand the impact of virus-laden droplets on indoor air quality

ORNL’s Sergei Kalinin and Rama Vasudevan (foreground) use scanning probe microscopy to study bulk ferroelectricity and surface electrochemistry -- and generate a lot of data. Credit: Jason Richards/ORNL, U.S. Dept. of Energy

At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.

ORNL Sign

Seven ORNL scientists have been named among the 2020 Highly Cited Researchers list, according to Clarivate, a data analytics firm that specializes in scientific and academic research.

The CrossVis application includes a parallel coordinates plot (left), a tiled image view (right) and other interactive data views. Credit: Chad Steed/Oak Ridge National Laboratory, U.S. Dept. of Energy

From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.

Edge computing is both dependent on and greatly influencing a host of promising technologies including (clockwise from top left): quantum computing; high-performance computing; neuromorphic computing; and carbon nanotubes.

We have a data problem. Humanity is now generating more data than it can handle; more sensors, smartphones, and devices of all types are coming online every day and contributing to the ever-growing global dataset.