A research team from the Institute of Physics, Chinese Academy of Sciences, has developed FastTrack, a new machine learning-based framework dedicated  to evaluate ion migration barriers in crystalline solids. By combining machine learning force field (MLFFs) with three-dimensional potential energy surface (PES) sampling and interpolation, FastTrack enables accurate prediction of atomic migration barriers within mere minutes. Unlike traditional methods such as density functional theory (DFT) and nudged elastic band (NEB), which can take hours or days per calculation. FastTrack offers a speedup of over 100 times without sacrificing accuracy, closely matching experimental and quantum-mechanical benchmarks. This powerful tool automatically identifies diffusion pathways, visualizes energy landscapes, and provides detailed microscopic insights into ion migration mechanisms, crucial for designing more efficient batteries, fuel cells, and other energy storage and conversion devices.

SUTD researchers have developed a streamlined life cycle assessment method that makes environmental evaluation faster, cheaper, and more accessible to product designers without compromising reliability.

Skeletal muscle regeneration research is hindered by the "photodamage-imaging quality" trade-off in three-photon microscopy (3PM). A team from Zhejiang University developed the Multi-Scale Attention Denoising Network (MSAD-Net) to address this: combining MSAD-Net with 3PM reduces excitation power to 1.0–1.5 mW (1/4–1/2 of conventional levels) and scanning time to 2–3 μs/pixel (1/6–1/4 of standard), while maintaining 0.9932 SSIM and real-time denoising (80ms/frame). The system enables five-channel deep in vivo imaging of mouse muscle, uncovering key roles of macrophages and blood vessels in muscle stem cell-mediated repair.

On Mars, dust devils and winds reach speeds of up to 160 km/h and are therefore faster than previously assumed: This shows a study by an international research team led by the University of Bern. The researchers analyzed images taken by the Bernese Mars camera CaSSIS and the stereo camera HRSC with the help of machine learning. The study provides a valuable data basis for a better understanding of atmospheric dynamics, which is important for better climate models and future Mars missions.

Combing through 20 years of images from the European Space Agency’s Mars Express and ExoMars Trace Gas Orbiter spacecraft, scientists have tracked 1039 tornado-like whirlwinds to reveal how dust is lifted into the air and swept around Mars’s surface.

Published today in Science Advances, their findings – including that the strongest winds on Mars blow much faster than we thought – give us a much clearer picture of the Red Planet’s weather and climate.

And with these ‘dust devils’ collected into a single public catalogue, this research is just the beginning. Besides pure science, it will be useful for planning future missions, for example incorporating provisions for the irksome dust that settles on the solar panels of our robotic rovers.