AI reveals unexpected new physics in dusty plasma
Peer-Reviewed Publication
Updates every hour. Last Updated: 20-Aug-2025 13:10 ET (20-Aug-2025 17:10 GMT/UTC)
Physicists used a machine-learning method to identify surprising new twists on the non-reciprocal forces governing a many-body system.
Researchers at Kumamoto University and Nagoya University have developed a new class of two-dimensional (2D) metal-organic frameworks (MOFs) using triptycene-based molecules, marking a breakthrough in the quest to understand and enhance the physical properties of these promising materials. This innovation opens new possibilities for multifunctional applications in gas/molecular sensors, electrochemical energy storage, and spintronic devices.
Could clothing monitor a person’s health in real time, because the clothing itself is a self-powered sensor? A new material created through electrospinning, which is a process that draws out fibers using electricity, brings this possibility one step closer.
MIT researchers designed a computationally efficient algorithm for machine learning with symmetric data that also requires fewer data for training than conventional approaches. Their work could inform the design of faster, more accurate machine-learning models for tasks like discovering new drugs or identifying astronomical phenomena.
Scientists have discovered a new way that matter can exist – one that is different from the usual states of solid, liquid, gas or plasma – at the interface of two exotic, materials made into a sandwich.
The new quantum state, called quantum liquid crystal, appears to follow its own rules and offers characteristics that could pave the way for advanced technological applications, the scientists said.
When it comes to susceptibility to influence on social media, “It’s not just about who you are—it’s about where you are in a network, and who you’re connected to,” said Luca Luceri, a lead scientist at USC’s Information Sciences Institute (ISI). A new study by Luceri and his team finds that the likelihood someone will be influenced online isn’t spread evenly across a social platform. Instead, it clusters.
They call this the Susceptibility Paradox; it’s a pattern in which users’ friends are, on average, more influenceable than the users themselves. And it may help explain how behaviors, trends, and ideas catch on—and why some corners of the internet are more vulnerable to influence than others.
A system developed at Texas A&M University uses drone imagery and artificial intelligence to rapidly assess damage after hurricanes and floods, offering life-saving insights in minutes.