Accelerating Materials Science with Tensor Networks
THOR AI Solves 100-Year-Old Physics Problem in Seconds
Researchers at UNM and Los Alamos use tensor networks to simulate atomic behavior faster than supercomputers.
A digital visualization of interconnected nodes and lines representing a tensor network overlaid on a 3D model of atoms in a crystal lattice.
Photo: Avantgarde News
Researchers at The University of New Mexico and Los Alamos National Laboratory introduced a new AI-powered framework called THOR [1]. The system uses tensor network mathematics to calculate the thermodynamic behavior of atoms in materials [1]. This breakthrough effectively solves a physics problem that has remained a computational challenge for 100 years [1]. THOR operates hundreds of times faster than traditional supercomputer simulations [1]. By applying tensor networks, the framework simplifies complex atomic interactions into manageable calculations [1]. This efficiency allows scientists to study material properties with unprecedented speed and precision [1].
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THOR AI framework solves a 100-year-old physics problem in seconds
Researchers at The University of New Mexico and Los Alamos National Laboratory introduced THOR, an AI-powered framework that uses tensor network mathematics to calculate the thermodynamic behavior of atoms in materials hundreds of times faster than traditional supercomputer simulations.
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Avantgarde News Desk covers accelerating materials science with tensor networks and editorial analysis for Avantgarde News.