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Machine learning has emerged as a powerful tool in condensed matter physics, offering new perspectives on the exploration of quantum many-body systems, phase transitions and exotic states of matter.

Researchers at Rice University report that cerium magnesium hexalluminate, a compound with the formula CeMgAl11O19, is not the quantum spin liquid it was long believed to be. In a peer-reviewed study ...

In condensed matter physics, analysis of symmetries is a fundamental theoretical technique used to classify phases of matter, constrain effective Hamiltonians, and predict emergent phenomena ...

Theoretical physicists have established a close connection between the two rapidly developing fields in theoretical physics, quantum information theory and non-invertible symmetries in particle and ...

During the last century, biologists have extensively studied Drosophila melanogaster, the common household fruit fly. It's become one of the most popular model organisms, but not because scientists ...

Describing natural phenomena with geometric language is one of the most important achievements of modern physics. In the context of general relativity, Einstein treats spacetime as a dynamic geometric ...

The Institute of Physics and IOP Publishing (IOPP) are launching Progress In Physics 2024, a two-day hybrid workshop hosted at the Institute of Physics' office in London from 9-10 October 2024. The ...

Physicists have uncovered new states of matter by exploring the behavior of flatland electrons in extreme conditions, revealing insights that could impact quantum computing and advanced materials.