THOR AI solves a 100-year-old physics problem in seconds
www.sciencedaily.com
A new AI framework called THOR is transforming how scientists calculate the behavior of atoms inside materials. Instead of relying on slow simulations that take weeks of supercomputer time, the system uses tensor network mathematics and machine-learning models to solve the problem directly. The approach can compute key thermodynamic properties hundreds of times faster while preserving accuracy. Researchers say this could accelerate discoveries in materials science, physics, and chemistry.

Original Reddit post

Researchers at The University of New Mexico and Los Alamos National Laboratory have introduced a new computational approach designed to solve one of the most difficult problems in statistical physics. Their system, called the Tensors for High-dimensional Object Representation (THOR) AI framework, uses tensor network algorithms to handle extremely large mathematical calculations known as configurational integrals, along with the partial differential equations needed to analyze materials. These calculations are essential for predicting the thermodynamic and mechanical behavior of materials. To make the system more powerful, the researchers combined the framework with machine learning potentials that capture how atoms interact and move. This integration allows scientists to model materials accurately and efficiently across a wide range of physical environments. “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,” said Los Alamos senior AI scientist Boian Alexandrov, who led the project. “Accurately determining the thermodynamic behavior deepens our scientific understanding of statistical mechanics and informs key areas such as metallurgy.” submitted by /u/PixeledPathogen

Originally posted by u/PixeledPathogen on r/ArtificialInteligence