Heavy polar molecules are some of the most sensitive tools physicists have for probing what lies beyond the Standard Model, the theory that describes the particles and forces we know about. But turning that sensitivity into precise, trustworthy measurements has long been held back by one stubborn problem: Stray electric and magnetic fields drown out the tiny signals researchers are actually looking for.

In most everyday materials, such as copper, silver and silicon, the behavior of electrons is relatively predictable. In quantum materials, however, electrons can interact in complex ways, giving rise to collective electronic states with remarkable properties. Understanding how these states emerge—and, ultimately, how to control them—is one of the central challenges in quantum materials research.

Reliably generating controlled miniature rotations has long been a challenge: Chemical propulsion systems wear out, and methods that use electric or magnetic fields require complex setups. A team from KIT’s Institute of Microstructure Technology (IMT) and the Suzhou Institute of Nano-tech and Nano-bionics (SINANO) at the Chinese Academy of Sciences has now demonstrated that flow at a water surface alone is sufficient to rotate a floating object in a fixed direction. Their research is published in the journal Science Advances.

In recent years, research using artificial intelligence to predict material properties has advanced rapidly. Neural network quantum Monte Carlo methods have attracted attention as highly accurate simulation techniques. However, their extremely high computational cost has limited their application to small molecular systems. This study introduces a new computational method that overcomes this limitation.

A research team led by UCLA and the University of Rochester has demonstrated a promising evolution of an imaging system designed to capture details within “complex media,” which scatter light, from depicting structures inside body tissue to seeing obstacles through heavy fog. The system uses physics-based machine learning to improve an existing imaging technique.

Logically, you would think a sleek surface has optimal aerodynamics—but recent research at Tohoku University turns this fundamental principle on its head. Applying an irregular microscale surface texture reduced the aerodynamic drag of a test model. The innovation has potential applications in the design of fuel-efficient vehicles. The study is published in the Journal of Fluid Mechanics.