A research team from the Institute of Modern Physics (IMP), Chinese Academy of Sciences (CAS), has developed a machine learning (ML) model enhanced with expert knowledge to classify eight types of faults in superconducting radio-frequency (SRF) cavities at the China Accelerator Facility for superheavy Elements (CAFE2) facility. This model helps operators quickly identify fault patterns after incidents, speeding recovery and supporting long-term stable SRF accelerator operation.

SAN ANTONIO — April 28, 2025 —The NASA New Horizons spacecraft’s extensive observations of Lyman-alpha emissions have resulted in the first-ever map from the galaxy at this important ultraviolet wavelength, providing a new look at the galactic region surrounding our solar system. The findings are described in a new study authored by the SwRI-led New Horizons team.

A squishy, layered material that dramatically transforms under pressure could someday help computers store more data with less energy. That’s according to a new study by researchers at Washington State University and the University of North Carolina at Charlotte that shows a hybrid zinc telluride-based material can undergo surprising structural changes when squeezed together like a molecular sandwich.

The interaction between growth and the active migration of cells plays a crucial role in the spatial mixing of growing cell colonies. This connection was discovered by scientists from the Department of Living Matter Physics at the Max Planck Institute for Dynamics and Self-Organization (MPI-DS). Their results provide new approaches to understanding the dynamics of bacterial colonies and tumors.

Metal oxyhydroxides are nanoparticles with wide industrial applications, but determining their exact structure is often challenging. Recently, a research team from the Japan Advanced Institute of Science and Technology has developed an advanced imaging method called “lattice correlation analysis” to reveal the detailed 3D atomic structure of titanium oxyhydroxide nanoparticles. By leveraging data-driven insights, this method unlocks the crystal attributes without causing any damage, thus marking a milestone in the study of sensitive nanomaterials.

Scientists at Columbia Engineering have created a machine learning algorithm that can observe the pattern produced by nanocrystals to infer the material’s atomic structure, as described in a new study published in Nature Materials.