RSS Nature Physics
Nature Physics offers news and reviews alongside top-quality research papers in a monthly publication, covering the entire spectrum of physics. Physics addresses the properties and interactions of matter and energy, and plays a key role in the development of a broad range of technologies. To reflect this, Nature Physics covers all areas of pure and applied physics research. The journal focuses on core physics disciplines, but is also open to a broad range of topics whose central theme falls within the bounds of physics.
Feed URL: https://www.nature.com/nphys.rss
Updated: daily
Feed URL: https://www.nature.com/nphys.rss
Updated: daily
- A way to cross the Andreev bridgeNature Physics, Published online: 10 July 2024; doi:10.1038/s41567-024-02575-0 Inducing superconductivity in quantum anomalous Hall insulators is crucial to realize topological superconductors. Now a study shows superconducting correlations in the quantum anomalous Hall state, which can convert electrons on its one-way path into holes.
- The biophysical basis of bacterial colony growthNature Physics, Published online: 09 July 2024; doi:10.1038/s41567-024-02572-3 The growth of a biofilm—a bacterial colony attached to a surface—is governed by a trade-off between horizontal and vertical expansion. Now, it is shown that this process significantly depends on the contact angle at the biofilm’s edge.
- Fully nonlinear neuromorphic computing with linear wave scatteringNature Physics, Published online: 09 July 2024; doi:10.1038/s41567-024-02534-9 As the energy consumption of neural networks continues to grow, different approaches to deep learning are needed. A neuromorphic method offering nonlinear computation based on linear wave scattering can be implemented using integrated photonics.
- Nonlinear computation with linear systemsNature Physics, Published online: 09 July 2024; doi:10.1038/s41567-024-02531-y Nonlinearity is crucial for sophisticated tasks in machine learning but is often difficult to engineer outside of electronics. By encoding the inputs in parameters of the system, linear systems can realize efficiently trainable nonlinear computations.
- Efficient optimization of deep neural quantum statesNature Physics, Published online: 09 July 2024; doi:10.1038/s41567-024-02567-0 An improved optimization algorithm enables the training of large-scale neural quantum states in which the enormous number of neuron connections capture the intricate complexity of quantum many-body wavefunctions. This advance leads to unprecedented accuracy in paradigmatic quantum models, opening up new avenues for simulating and understanding complex quantum phenomena.
- Revealing the complex phases of rhombohedral trilayer grapheneNature Physics, Published online: 08 July 2024; doi:10.1038/s41567-024-02561-6 Rhombohedral graphene is an emerging material with a rich correlated-electron phenomenology, including superconductivity. The magnetism of symmetry-broken trilayer graphene has now been explored, revealing important details of the physics and providing a roadmap for broader explorations of rhombohedral graphene.
- Constants in disguiseNature Physics, Published online: 08 July 2024; doi:10.1038/s41567-024-02583-0 It has many names and yet no name. The designation of the universal gas constant as R has remained a mystery, as Karen Mudryk recounts.
- Active hydraulics and odd elasticity of muscle fibresNature Physics, Published online: 08 July 2024; doi:10.1038/s41567-024-02540-x A multiscale model of muscle as a fluid-filled sponge suggests that hydraulics limits rapid contractions and that the mechanical response of muscle is non-reciprocal.
