Research in the lab of UC Santa Barbara materials professor Stephen Wilson is focused on understanding the fundamental physics behind unusual states of matter and developing materials that can host the kinds of properties needed for quantum functionalities.

Can a small lump of metal be in a quantum state that extends over distant locations? A research team at the University of Vienna answers this question with a resounding yes. In the journal Nature, physicists from the University of Vienna and the University of Duisburg-Essen show that even massive nanoparticles consisting of thousands of sodium atoms follow the rules of quantum mechanics. The experiment is currently one of the best tests of quantum mechanics on a macroscopic scale. 

During the last ice age, the Atlantic Ocean’s powerful current system remained active and continued to transport warm, salty water from the tropics to the North Atlantic despite extensive ice cover across much of the Northern Hemisphere, finds new research led by UCL scientists.

In a world first, a research team led by the University of Oxford’s Department of Engineering Science has shown it is possible to engineer a quantum mechanical process inside proteins, opening the door to a new class of quantum-enabled biological technologies. The study has been published today (21 January) in Nature.

A research team of The Hong Kong Polytechnic University (PolyU) has achieved a revolutionary breakthrough in smart materials, successfully developing soft magnetorheological textiles that can flexibly deform and modulate their mechanical properties under a human-safe magnetic field. Driven by electricity and programmable control, these new materials combine lightweight, flexible and breathable textile characteristics, making them widely applicable in smart wearables, soft robotics, virtual reality and metaverse haptic experiences.

Generative AI models can propose molecular structures guided by target properties, compressing what once took years of trial-and-error into hours of computation. A team of researchers has now developed a new method that advances this capability even further. The method, PropMolFlow (Property-guided Molecular Flow), can generate molecular candidates roughly 10 times faster than existing methods—and without compromising the accuracy or chemical validity of the results.