A NIMS research team has developed a new experimental method capable of rapidly evaluating numerous material compositions by measuring anomalous Hall resistivity 30 times faster than conventional methods. By analyzing the vast amount of data obtained using machine learning and experimentally validating the predictions, the team succeeded in developing a new magnetic sensor material capable of detecting magnetism with much higher sensitivity. This research was published in npj Computational Materials on September 3, 2025.

New mathematical tools shed light on the fluctuations of living matter

Fluctuations in such energy-consuming systems cannot be assessed by traditional physics due to the influence of the arrow of time on their behavior

Quantitative predictions on the behavior of active matter can facilitate the experimental design of such systems

To answer this question: Can generative AI improve vehicle trajectory prediction in car-following scenarios? Researchers from the University of Wisconsin–Madison, Tongji University, and collaborators developed FollowGen, a conditional diffusion model that integrates historical motion features and inter-vehicle interactions to generate safer and more reliable trajectory predictions for autonomous driving.