Researchers at ORNL Demonstrate Scalability of GNNs on World’s Most Powerful Computing Systems

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Graph Nueral Networks
Conversion of an atomic structure into a graph, where atoms are treating as nodes and interatomic bonds as edges (credit: Massimiliano “Max” Lupo Pasini/ORNL, U.S. Dept. of Energy).

February 13, 2024 | Originally published by ORNL on January 18, 2024

Solving today’s most complex scientific challenges often means tracing links between hundreds, thousands, or even millions of variables. The larger the scientific dataset, the more complex these connections become.

With experiments generating petabytes and even exabytes of data over time, tracking the connections in processes such as drug discovery, materials development, or cybersecurity can be a Herculean task.

Thankfully, with the advent of artificial intelligence, researchers can rely on graph neural networks, or GNNs, to map the connections and unravel their relationships, greatly expediting time to solution and, by extension, scientific discovery.