Physicists discover a new magnetic phase

In the SLAC National Accelerator Laboratory (SLAC), the U.S. Department of Energy’s Laboratory in collaboration with the European Synchrotron Radiation Facility uncovered confirmatory evidence of a third magnetic phase: altermagnetism. The research team published their findings in Science this week. Altermagnetism has potential applications in next-generation electronics like memory devices, data centers and quantum technologies.

For decades, physicists recognized ferromagnetism and antiferromagnetism as the only two unique magnetic phases that occur in a material without external influence. First theorized in 2022, altermagnetism has now been confirmed by researchers who have been compiling experimental evidence for years. “Scientists have observed phenomena that don’t fit neatly into the ferromagnetism-antiferromagnetism dichotomy,” lead author Daniel Jost, associate scientist at SLAC and Stanford Institute of Materials and Energy Sciences, told the Stanford Report. 

Building on prior studies, the research has experimentally demonstrated phenomena in manganese telluride (MnTe) that are impossible to explain without altermagnetism, indicating that MnTe is an altermagnet. The revelation that led to the discovery was chirality: MnTe has an asymmetric magnetic structure that alternates in up-down motions. Unlike ferromagnets, the opposite magnetic spins are locked to the crystal structure itself, requiring the rotation of the lattice to match them. This specific “structural locking” causes the spins to exhibit a zero net magnetization while retaining magnetism in smaller regions, blending high-speed utility of ferromagnets with the stable, field-free nature of antiferromagnets. Manipulating these properties could make them ideal candidates for advanced electronics.

The discovery of altermagnets opens new possibilities for the field of spintronics, where scientists are utilizing the positive and negative spins of magnetic materials to design computing technologies. “Ferromagnets are too easily influenced by magnetic fields,” Jost said. “[Antiferromagnets are] practically invisible – so you can’t easily read the data stored in them or use them to drive efficient spin currents.” Altermagnets are a combination of the two established classes without their limitations. 

Fingernail-sized chip holds potential to advance microscopy, astronomy and fields of imaging 

For the first time, a photonic computer chip the size of a fingernail can process light inputs from different laser sources and organize the photons into clear sets of distinct beams. The chip uses photons instead of a standard electrical current to transmit and manipulate information. The Stanford research and chip engineering team published their findings in Nature Photonics, opening the door to numerous applications in imaging systems.

The study aimed to untangle different rhythms of photons contained in light bulb-style light to enable new mediums of communication, computing and sensing. By engineering a way to distinguish between different light sources, researchers could collect more information from light to advance camera, microscope and telescope technologies. “This work has a huge bearing on a whole bunch of different applications because it allows us to think about [light] in a totally new way,” co-lead author Annie Kroo Ph.D. ’26, told the Stanford Report. 

The researchers used the tiny chip to channel photons for data processing, like how cellphones use electrons to power internal circuits. The algorithms inside the chip detect unique signatures from mixed light and sort each component into coherent, predictable beams. “Our chip learns the underlying coherence modes of an unknown light field and physically directs them into separate outputs,” said co-author and engineering professor Shanhui Fan. “That gives us a way to analyze the light’s spatial coherence while keeping the separated light available for further processing.”

By separating mixed light, the chip allows researchers to collect more utility from the input light compared to even the most sophisticated cameras due to the chip’s depth retention. “In a camera, we throw away the depth information,” said co-author David Miller, professor of electrical engineering. “By doing this separation of light, we can get whatever depth information there is.” 

Using their new technology, the research team plans to work with NASA to probe light from distant galaxies. “Light is a beautiful medium for measuring things in the environment,” Kroo said,

The Stanford Digital Brain Project plans to build an AI model that simulates human neural activity

The Stanford Digital Brain Project plans to develop a predictive foundation model of the human brain to accelerate neuroscience research and clinical applications, including the detection and treatment of brain disorders. 

Headquartered in the Wu Tsai Neuroscience Institute, the project unites faculty from across the University, such as the schools of Humanities and Sciences, Medicine and Engineering, who will collaborate over the next five years to create large, curated datasets of neural activity and use them to train the most accurate models of the human brain ever attempted.

The research team, co-led by Daniel Yamins and Russel Poldrack, plans to collect data from small cohorts and engage in deep data collection, measuring an individual’s brain multiple times. Rather than isolating neural activity, the project plans to have its model trained on large-scale naturalistic brain imaging data and ultra-high-field 7-Tesla Magnetic Resonance Imaging (MRI) scanners. Participants will also wear a helmet called the Optically Pumped Magnetometer Magnetoencephalography (OPM-MEG), a brain scanner that measures the tiny magnetic fields produced when brain cells communicate. 

“By training foundation models on brain data at scale, we’ll be able to form quantitative predictions of how brain circuits process information,” Yamins, a Wu Tsai Neuro faculty scholar and an associate professor of psychology and of computer science, told the Stanford Report.

Upon creation of the digital human brain, researchers could query the model to identify evasive neural network dysfunctions underlying disorders including depression or Parkinson’s disease. “The best way to figure out how to fix a system – whether it’s a car, a heart, or the brain – is to build a detailed model that lets you compare what it looks like when it’s in working condition and when it’s not,” Yamins said. Both the comprehensive model and the dataset involved in constructing the artificial intelligence could offer patients personalized treatment.