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Keyword：machine learning
U.S. STREAMLINE Project Advances Nuclear Quantum Many-Body Research with Artificial Intelligence
On August 25, 2026, a new project named STREAMLINE is combining artificial intelligence, machine learning, and supercomputing to address the nuclear quantum many-body problem in nuclear physics research, aiming to more accurately simulate the interactions between protons and neutrons within atomic nuclei and provide theoretical support for frontier topics such as neutron stars and fundamental interactions. The difficulty of the nuclear quantum many-body problem lies in the fact that as the number of particles increases, the possible interactions among them grow rapidly, and traditional computational methods quickly hit the ceiling of computing power. Even with high-performance supercomputers, only quantum systems of relatively limited scale can typically be handled. The STREAMLINE project hopes to...
2026-08-26
Russian team develops machine learning model to predict stability of rare earth and actinide complexes
Researchers from the Interdisciplinary Laboratory for Intelligent Chemical Design at the Department of Chemistry of Lomonosov Moscow State University, in collaboration with colleagues from the Department of Mechanics and Mathematics, have developed a new machine learning model for evaluating the stability of complexes of rare earth elements and trivalent actinides. The results have been published in the Journal of Chemical Physics. The research team stated that machine learning can reduce time and material costs in chemical research, which is particularly important for rare, expensive, or hazardous lanthanides and actinides. Lanthanides, along with scandium and yttrium, are commonly classified as rare earth elements and are widely used in permanent magnets, batteries, electronic components, lasers, and other applications. Since rare earth elements in nature...
2026-08-25
Argonne National Laboratory Unveils DONUT Tool to Accelerate Real-Time Analysis of X-ray Nanodiffraction Data
Researchers at the U.S. Department of Energy's Argonne National Laboratory have developed a new machine learning tool called DONUT to accelerate X-ray data analysis in experiments at the Advanced Photon Source (APS). The tool can process complex images generated by scanning X-ray nanodiffraction microscopy (SXDM) in real time during experiments, helping researchers more quickly determine internal structural changes in materials. DONUT stands for "Nanobeam Optical Diffraction based on Unsupervised Training," a physics-aware neural network. Its key feature is combining artificial intelligence methods with physical models of focused X-ray beam interactions with materials, allowing it to learn directly from experimental data without relying on pre-labeled training...
2026-08-17
Machine learning framework predicts micro-displacements of DIII-D fusion device coils
Researchers at the U.S. Department of Energy's (DOE) Thomas Jefferson National Accelerator Facility (Jefferson Lab) and collaborating teams have developed a machine learning framework that can predict subtle changes in critical fusion device hardware before the next experiment begins. The method targets the DIII-D National Fusion Facility in San Diego, and the findings have been published in the journal Machine Learning with Applications. Interior of the DIII-D National Fusion Facility tokamak. (Image courtesy of General Atomics) DIII-D is a tokam...
2026-08-17
Sophelio Launches Fusion Equilibrium Challenge, Opening DIII-D and MAST Experimental Datasets
On August 11, Sophelio launched the Fusion Equilibrium Challenge. The competition has been accepted into the 2026 Conference on Neural Information Processing Systems (NeurIPS) competition track, making it the first fusion energy-related challenge to enter this track. The challenge is co-organized by Sophelio together with the DIII-D National Fusion Facility, the UK Atomic Energy Authority (UKAEA) FAIR-MAST project, and the Institute for Fusion Studies (IFS) at the University of Texas at Austin, with data hosted on the Hugging Face platform. Open to the global machine learning community, the competition re...
2026-08-12