For over a century, one of physics’ toughest challenges has puzzled scientists: the configurational integral, a mind-bending calculation that determines how particles interact inside materials. Solving it directly was considered nearly impossible-even the fastest supercomputers needed weeks and still fell short.

Now, researchers at the University of New Mexico and Los Alamos National Laboratory have unveiled THOR AI (Tensors for High-dimensional Object Representation), a groundbreaking framework that has stunned the scientific world. This system uses advanced tensor networks and machine learning to break down unimaginably complex equations into simpler parts, completing them in seconds instead of thousands of hours-without losing accuracy.

Applied to materials like copper, argon, and tin, THOR AI produced results that were 400 times faster than traditional simulations. Scientists believe this breakthrough will completely transform material science, metallurgy, and chemistry-offering faster discoveries in everything from stronger metals to advanced electronics.

Published in Physical Review Materials, the findings are already being hailed as a turning point. As lead researcher Boian Alexandrov said: “This replaces century-old guesswork with first-principles science.”