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The AI agent, incorporating a language model-based molecular generator and a graph neural network-based molecular property predictor, processes a set of user-provided molecules (green) and produces/suggests new molecules (red) with desired chemical/physical properties (i.e. excitation energy). Image credit: Pilsun You, Jason Smith/ORNL, U.S. DOE

A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules. 

ORNL researchers are establishing a digital thread of data, algorithms and workflows to produce a continuously updated model of earth systems.

Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.

 

Scientists at Oak Ridge National Laboratory contributed to several chapters of the Fifth National Climate Assessment, providing expertise in complex ecosystem processes, energy systems, human dynamics, computational science and Earth-scale modeling. Credit: ORNL, U.S. Dept. of Energy

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.

Frontier’s exascale power enables the Energy, Exascale and Earth System Model-Multiscale Modeling Framework — or E3SM-MMF — project to run years’ worth of climate simulations at unprecedented speed and scale. Credit: Mark Taylor/Sandia National Laboratories, U.S. Dept. of Energy

The world’s first exascale supercomputer will help scientists peer into the future of global climate change and open a window into weather patterns that could affect the world a generation from now.

Attendees of SMC23 pose for their annual group photo in downtown Knoxville, TN.

ORNL hosted its annual Smoky Mountains Computational Sciences and Engineering Conference in person for the first time since the COVID-19 pandemic.

Director of ORNL’s AI Initiative Prasanna Balaprakash addresses attendees at the Generative AI for ORNL Science Workshop. Credit: ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory hosted its Smoky Mountains Computational Science and Engineering Conference for the first time in person since the COVID pandemic broke in 2020. The conference, which celebrated its 20th consecutive year, took place at the Crowne Plaza Hotel in downtown Knoxville, Tenn., in late August.

Steven Hamilton, an R&D scientist in the HPC Methods for Nuclear Applications group at ORNL, leads the ExaSMR project. ExaSMR was developed to run on the Oak Ridge Leadership Computing Facility’s exascale-class supercomputer, Frontier. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

The Exascale Small Modular Reactor effort, or ExaSMR, is a software stack developed over seven years under the Department of Energy’s Exascale Computing Project to produce the highest-resolution simulations of nuclear reactor systems to date. Now, ExaSMR has been nominated for a 2023 Gordon Bell Prize by the Association for Computing Machinery and is one of six finalists for the annual award, which honors outstanding achievements in high-performance computing from a variety of scientific domains.  

A rendering of the CFM RISE program’s open fan architecture. (bottom) A GE visualization of turbulent flow in the tip region of an open fan blade using the Frontier supercomputer at ORNL. Credit: CFM, GE Research (CFM is a 50­–50 joint company between GE and Safran Aircraft Engines)

Outside the high-performance computing, or HPC, community, exascale may seem more like fodder for science fiction than a powerful tool for scientific research. Yet, when seen through the lens of real-world applications, exascale computing goes from ethereal concept to tangible reality with exceptional benefits.

Cody Lloyd stands in front of images of historical nuclear field testing. The green and red dots are the machine learning algorithm recognizing features in the image. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Cody Lloyd became a nuclear engineer because of his interest in the Manhattan Project, the United States’ mission to advance nuclear science to end World War II. As a research associate in nuclear forensics at ORNL, Lloyd now teaches computers to interpret data from imagery of nuclear weapons tests from the 1950s and early 1960s, bringing his childhood fascination into his career

Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

After completing a bachelor’s degree in biology, Toya Beiswenger didn’t intend to go into forensics. But almost two decades later, the nuclear security scientist at ORNL has found a way to appreciate the art of nuclear forensics.