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Event generators are used to simulate and describe hadronic collisions in accelerator experiments, but often struggle to describe data from astroparticle experiments that probe hadronic collisions at extreme energies. This Review highlights the complementarity between accelerator and astroparticle experiments that can be exploited, to gain new insights into the nature of hadronic collisions and increase model accuracy across both domains.
This Technical Review highlights advances in cryo-electron microscopy for materials science, covering sample preparation, low-dose imaging and analytical scanning transmission electron microscopy techniques. It discusses strategies to mitigate electron-beam damage and enable high-resolution 2D and 3D imaging of beam-sensitive energy materials.
Tensor networks provide a powerful tool for understanding and improving quantum computing. This Technical Review discusses applications in simulation, circuit synthesis, error correction and mitigation, and quantum machine learning.
Exoplanetary atmosphere studies rely on the quality of laboratory spectroscopy and observational astronomy. This Technical Review highlights the power of high-resolution cross-correlation spectroscopy for advancing the field for all stakeholders.
The renormalization group (RG) is a theoretical framework to transform systems across scales and identify critical points of phase transitions. In recent years, efforts have extended RG to complex networks, which challenge traditional assumptions. This Technical Review covers key approaches and open challenges.
This Technical Review demonstrates how first-principles calculations and effective modelling provide realistic insights into spin–orbit interactions and their engineering in 2D materials and van der Waals heterostructures.
A better understanding of electronic transport in semiconductors is essential for both fundamental and applied physics, as it directly affects key material properties such as the conductivity and thermoelectric quantities. This Technical Review explores different frameworks and computational tools available for computing these properties of semiconductors.
Slow heterogeneous dynamics and the absence of visible structural order make it difficult to numerically and theoretically investigate glass-forming materials. This Technical Review outlines the role that machine learning tools can have and identifies key challenges, possible approaches and appropriate benchmarks.
