It’s been said that a finely tuned ear knows the size and shape of a piano by merely listening to the instrument’s notes. An international team of physicists has now devised an analogous approach to detect the universe’s hidden particles at high energies—opening a potential pathway for discovering new laws of physics.

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Researchers at the University of Würzburg have succeeded in detecting exceptionally robust electrical transport in a topological insulator. This could lead to new metrological applications. The work is published in the journal Nature Communications.

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Animals and electronic devices aren’t the only things with memory. Materials can retain memories of past deformations in their microscopic structure. A common example is a crease in a sheet of paper that has been folded then unfolded. Understanding this type of memory could benefit the design of materials that respond to changes in their […]

What if the Higgs boson found in 2012 is not alone but is the only sibling we have encountered so far? Scientists at CERN discovered the particle that year, and it was a major discovery because it explained how other particles acquire mass. For a long time, scientists thought this was the final piece of […]

Physicists have discovered a surprisingly simple way to reproduce one of the most fascinating models in modern physics—linked to black holes, quantum chaos and exotic electronic materials—using ultracold atoms trapped in light.

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Heavy polar molecules are some of the most sensitive tools physicists have for probing what lies beyond the Standard Model, the theory that describes the particles and forces we know about. But turning that sensitivity into precise, trustworthy measurements has long been held back by one stubborn problem: Stray electric and magnetic fields drown out […]

Quantum information is notoriously fragile. Internet traffic is anything but. Yet Northwestern University scientists have demonstrated they can peacefully coexist inside the same fiber-optic cable.

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In recent years, research using artificial intelligence to predict material properties has advanced rapidly. Neural network quantum Monte Carlo methods have attracted attention as highly accurate simulation techniques. However, their extremely high computational cost has limited their application to small molecular systems. This study introduces a new computational method that overcomes this limitation.

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