AI solves one of the biggest problems in physics considered impossible: “First principles calculation”
A problem that, using classical techniques, required calculation times that would exceed the age of the universe.
At a time when artificial intelligence seems capable of achieving anything, a group of researchers from the University of New Mexico and Los Alamos National Laboratory have developed a computational framework that addresses a major challenge in statistical physics. The framework in question is known as THOR, which stands for Tensors for High-dimensional Object Representation. It uses tensor network algorithms to efficiently compress and evaluate extremely large configuration integrals and partial differential equations, which are fundamental to determining the thermodynamic and mechanical properties of materials.
Boian Alexandrov, an AI scientist at Los Alamos who led this project, points out that “The configurational integral — which captures particle interactions — is notoriously difficult and time-consuming to evaluate, particularly in materials science applications involving extreme pressures or phase transitions. Accurately determining the thermodynamic behavior deepens our scientific understanding of statistical mechanics and informs key areas such as metallurgy”
A problem that can now be solved quickly and accurately
The configuration integral has long been considered impossible to solve, as pointed out by Dimiter Petsev, professor in the Department of Chemical and Biological Engineering at the University of New Mexico, who also highlights the fact that “classical integration techniques would require computational times exceeding the age of the universe, even with modern computers.”
Now, thanks to methods such as THOR, there is a new standard for quickly and accurately calculating the configurational integral. As Duc Truong, a scientist at Los Alamos, points out, “This breakthrough replaces century-old simulations and approximations of configurational integral with a first-principles calculation.”
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