{"id":21461,"date":"2026-10-15T12:08:50","date_gmt":"2026-10-15T12:08:50","guid":{"rendered":"https:\/\/news.theck1.no\/?p=21461"},"modified":"2026-10-15T12:08:50","modified_gmt":"2026-10-15T12:08:50","slug":"jamming-the-flow-to-enable-memory-in-quantum-materials","status":"publish","type":"post","link":"https:\/\/news.theck1.no\/?p=21461","title":{"rendered":"Jamming the flow to enable memory in quantum materials"},"content":{"rendered":"

AI-rewritten:<\/strong> This is a summary of an article from Phys.org, rewritten by AI (Qwen, running locally) to make it easier to read. The facts come from the original article – read it for the full story.<\/p>\n

Phys.org • October 14, 2026<\/strong><\/div>\n\n

Researchers at UC San Diego led by physics professor Oleg Shpyrko developed a new method to observe how vanadium dioxide behaves inside a neuristor, a device designed to mimic brain neurons. Using coherent X-ray diffraction, they measured the material while electricity was applied and discovered a way to make it learn specific states and then forget them. This capability is essential for creating energy-efficient hardware that emulates biological computing processes.<\/p>\n

<\/p>\n

The underlying mechanism involves a phase transition where the material shifts from a liquid-like state to a jammed or glassy state. The article explains this by comparing the material to passengers on a crowded train platform. When few people are present, they move independently; however, when the crowd is dense, one person’s movement affects others. In the neuristor, jamming the flow allows the system to learn, while restoring free flow erases its memory.<\/p>\n

These findings, published in Nature Communications by Nicol\u00f2 D’Anna et al., suggest a practical path toward brain-inspired technology. Controlling how these devices learn and forget remains central to their operation. The study highlights that manipulating this specific phase transition could lead to significant advancements in both fundamental materials science and the development of efficient neuromorphic computers.<\/p>\n

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Source:<\/strong> Phys.org • October 14, 2026<\/p>\n

Read the original article at Phys.org →<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

AI-rewritten: This is a summary of an article from Phys.org, rewritten by AI (Qwen, running locally) to make it easier to read. The facts come from the original article – read it for the full story. Phys.org • October 14, 2026 Researchers at UC San Diego led by physics professor Oleg Shpyrko developed a new<\/p>\n

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