One of the shared, fundamental goals of most chemistry researchers is the need to predict a molecule's properties, such as its boiling or melting point. Once researchers can pinpoint that prediction, they're able to move ...
For decades, medium-range weather forecasting—predicting conditions 1 to 5 days ahead—has relied heavily on traditional numerical models. However, this approach often struggles when applied to specific regions with limited ...
Researchers at the University of Osaka have developed a deep learning model for rapid building damage assessment after floods using satellite imagery. This research establishes the first systematic benchmark for this task ...
Although outbreaks of Ebola virus are rare, the disease is severe and often fatal, with few treatment options. Rather than targeting the virus itself, one promising therapeutic approach would be to interrupt proteins in the ...
The accumulation of misfolded proteins in the brain is central to the progression of neurodegenerative diseases like Huntington's, Alzheimer's and Parkinson's. But to the human eye, proteins that are destined to form harmful ...
In 2023, a train carrying hazardous materials derailed in East Palestine, Ohio. In 2025, a series of destructive wildfires ravaged Los Angeles. In both cases, a toxic plume—a cloud of harmful airborne materials that disperse ...
People have long wondered what life was first like on Earth, and if there is life in our solar system beyond our planet. Scientists have reason to believe that some of the moons in our solar system—like Jupiter's Europa and ...
Earth's continents may look fixed on a globe, but they've been drifting, splitting and reforming over billions of years—and they still are. Our new study reveals fresh evidence of rhythmic pulses of molten rock rising beneath ...
Researchers from the University of Tokyo in collaboration with Aisin Corporation have demonstrated that universal scaling laws, which describe how the properties of a system change with size and scale, apply to deep neural ...
An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin has introduced CGSchNet, a machine-learned coarse-grained (CG) model that can accurately and efficiently ...
