NEWSROOM

Researchers in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a machine learning framework that can predict with quantum-level accuracy how materials respond to electric fields, up to the scale of a million atoms – vastly accelerating simulations beyond quantum mechanical methods, which can model only a few hundred atoms at a time.

Quantum materials exhibit remarkable emergent properties when they are excited by external sources. However, these excited states decay rapidly once the excitation is removed, limiting their practical applications. A team of researchers from Harvard University and the Paul Scherrer Institute PSI have now demonstrated an approach to stabilise these fleeting states and probe their quantum behaviour using bright X-ray flashes from the X-ray free electron laser SwissFEL at PSI. T

MIT physicists have demonstrated a new form of magnetism that could one day be harnessed to build faster, denser, and less power-hungry “spintronic” memory chips. The new magnetic state is a mash-up of two main forms of magnetism: the ferromagnetism of everyday fridge magnets and compass needles, and antiferromagnetism, in which materials have magnetic properties at the microscale yet are not macroscopically magnetized.

An international study published in Nature Materials has revealed vortex-like structures in an antiferroelectric material — a class of material with no net polarity. This unprecedented discovery challenges classical theories and opens up exciting new possibilities in materials physics.

Using a new technique, the ETH researchers succeeded in creating the first lithium niobate metalenses with precisely engineered nanostructures. While functioning as normal light focusing lenses, these devices can simultaneously change the wavelength of laser light. When infrared light with a wavelength of 800 nanometres is sent through the metalens, visible radiation with a wavelength of 400 nanometres emerges on the other side and is directed at a designated point.

If commercialized, this technology is expected to enable the development of memory devices that are tens of times smaller and faster than current models. Consequently, the storage capacity and processing speed of smartphones and computers could be significantly improved, accelerating advancements in high-speed data processing technologies such as artificial intelligence (AI) and autonomous vehicles.