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ORNL’s Tomás Rush examines a culture as part of his research into the plant-fungus relationship that can help or hinder ecosystem health. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

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

Prasanna Balaprakash, who leads ORNL’s AI Initiative, participated in events hosted by the White House Office of Science and Technology Policy and the Task Force on American Innovation to discuss the challenges and opportunities posed by AI. Credit: Brian Mosley/Computing Research Association

In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the U.S. National Science Foundation.

Photo by James Wainscoat on Unsplash.

A team of researchers from the University of Southern California, the Renaissance Computing Institute at the University of North Carolina, and Oak Ridge, Lawrence Berkeley and Argonne National Laboratories have received a grant from the U.S. Department of Energy to develop the fundamentals of a computational platform that is fault tolerant, robust to various environmental conditions and adaptive to workloads and resource availability.

A researcher plays checkers against an AI-powered robotic arm in 1984. Credit: ORNL, U.S. Dept. of Energy

Despite its futuristic essence, artificial intelligence has a history that can be traced through several decades, and the ORNL has played a major role. From helping to drive fundamental and applied AI research from the field’s early days focused on expert systems, computer programs that rely on AI, to more recent developments in deep learning, a form of AI that enables machines to make evidence-based decisions, the lab’s AI research spans the spectrum.

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. 

Image of circuitry representing AI.

Research performed by a team, including scientists from ORNL and Argonne National Laboratory, has resulted in a Best Paper Award at the 19th IEEE International Conference on eScience.

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.

The DEMAND single crystal diffractometer at the High Flux Isotope Reactor, or HFIR, is the latest neutron instrument at the Department of Energy’s Oak Ridge National Laboratory to be equipped with machine learning-assisted software, called ReTIA. Credit: Jeremy Rumsey/ORNL, U.S. Dept. of Energy

Neutron experiments can take days to complete, requiring researchers to work long shifts to monitor progress and make necessary adjustments. But thanks to advances in artificial intelligence and machine learning, experiments can now be done remotely and in half the time.