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.