Choosing how to live in a way that truly helps our overburdened planet can be difficult. Researchers at DTU have calculated the impact of a wide range of everyday activities to help consumers lighten their impact on the environment.

Researchers have shown for the first time how hidden motions could control how granular materials such as soil and snow slip and slide, confirming a long-suspected hypothesis. The knowledge could help in understanding how landslides and avalanches work and even help the construction industry in the future.

Shanghai, August 21, 2025 — Nuclear energy is widely recognized as one of the most promising clean energy sources for the future, but its safe and efficient use depends critically on the development of robust nuclear fuels and structural materials that can endure extreme environments. A newly published review in AI & Materials highlights how machine learning (ML) is transforming this field, enabling scientists to accelerate discoveries, optimize performance, and overcome long-standing challenges in nuclear materials research.

The article, titled “Machine learning in research and development of advanced nuclear materials: a systematic review for continuum-scale modelling,” was authored by Chaoyue Jin and Professor Shurong Ding from the Institute of Mechanics and Computational Engineering at Fudan University. It provides an in-depth systematic review of how ML has been applied to nuclear fuels and structural materials, particularly at the continuum scale.

A new study published in the Proceedings of the National Academy of Sciences by an interdisciplinary team of researchers challenges long-held assumptions about the origins of agriculture