In the 17th century, astronomers Christiaan Huygens and Giovanni Cassini trained their telescopes on Saturn and uncovered a startling truth: the planet's luminous bands were not solid appendages, but vast, separate rings ...
Researchers from The University of New Mexico and Los Alamos National Laboratory have developed a novel computational framework that addresses a longstanding challenge in statistical physics.
Plutonium has captured the attention of scientists since its discovery in the early 1940s. This enigmatic element has an important role to play in emerging energy technologies like nuclear batteries and reactors, but it also ...
A research team has developed a novel direct sampling method based on deep generative models. Their method enables efficient sampling of the Boltzmann distribution across a continuous temperature range. The findings have ...
In the past decade, astronomers have witnessed three interstellar objects (ISOs) passing through the solar system. These include the enigmatic 'Oumuamua in 2017, the interstellar comet 2I/Borisov in 2019, and 3I/ATLAS in ...
Topological quantum systems are physical systems exhibiting properties that depend on the overall connectivity of their underlying lattice, as opposed to local interactions and their microscopic structure. Predicting the ...
The endangered South African cycad Encephalartos horridus may resemble a relic from the Jurassic age, but the species itself evolved long after dinosaurs disappeared. Still, it carries a biochemical legacy inherited from ...
In the ERC project HelixMold, a team from TU Graz developed a method for the computer-based design of artificial proteins, with a focus on custom biocatalysts for pharmaceutical applications or the degradation of biopolymers.
One of ANSTO's advanced imaging instruments Dingo now delivers a rare fusion of simulation and radiobiology, becoming a launchpad for an innovative neutron therapy innovation.
At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don't have much data. Conversely, successful machine learning (ML) tends to rely on large, high-quality data sets for training. ...
