Ionization of molecules by ultrashort extreme ultraviolet and X-ray pulses triggers complex ultrafast electron-nuclear dynamics central to attochemistry. Unlike atoms, molecules feature multicentric potentials, reduced symmetry, and intrinsic vibrational motion, all of which complicate the theoretical description of continuum states and call for approaches that extend beyond...
Recent advances in machine learning, particularly in multimodal models, have created new opportunities for analyzing complex data in high-energy physics, where accurate identification of particle interactions is critical for scientific discovery. However, existing approaches rely heavily on convolutional neural networks, which lack interpretability and do not fully leverage...
The pseudogap in high-temperature superconducting cuprates is an exotic state of matter, displaying emerging Fermi arcs and a momentum-selective suppression of spectral weight upon cooling. We show how these phenomena are captured in the three-band Emery model by performing dynamical vertex approximation calculations for its normal state. For the hole-doped parent compound, our...
Quantum neural networks (QNNs), typically implemented via parameterized quantum circuits (PQCs), offer a potential route to quantum advantage in learning. We review recent progress through the Fourier structure of PQCs: the encoding Hamiltonian fixes the accessible frequency set, while training estimates the corresponding Fourier coefficients. This perspective explains how QNNs...
We investigate the roughening transition of an electric flux string between two static charges in a \({{\mathbb{Z}}}_{2}\) lattice gauge theory in (2+1) dimensions. This transition is compelling because of its relation to the continuum limit. However, the entanglement growth makes it harder to access it computationally. Using numerical simulations with matrix product states, we...
As scientific instruments and the literature generate ever larger volumes of data, machine learning (ML) has become essential for organizing, analyzing and interpreting complex information. This Perspective examines how ML accelerates discovery across disciplines, with examples such as brain mapping and exoplanet detection. It also considers situations with different levels of...
Understanding Coulomb driven many-body dynamics in ultracold atomic systems far from equilibrium remains an open challenge, particularly when ultrafast excitation channels create competing pathways toward Rydberg gases or ultracold plasmas. Here, we investigate the many-body dynamics in a 87Rb Bose-Einstein condensate after exposure to a single femtosecond laser pulse. By tuning...
The inherent complexity of biological agents often leads to motility behavior that appears to have random components. Robust stochastic inference methods are therefore required to understand and predict the motion patterns from time-discrete trajectory data provided by experiments. In many cases, second-order Langevin models are needed to adequately capture the motility...
Direct detection experiments have established the most stringent constraints on potential interactions between particle candidates for relic, thermal dark matter and Standard Model particles. To surpass current exclusion limits a new generation of experiments is being developed. The upcoming upgrade of the CRESST experiment will incorporate \({{{\mathcal{O}}}}\)(100) detectors...
The emergence of order from an initially uncorrelated state across a phase transition is a central problem in quantum many-body physics, particularly in multicomponent systems where interactions between components lead to rich nonequilibrium dynamics. While defect formation is known to follow universal scaling laws, prior studies have focused mainly on defect density, leaving...
Complex functionalities in biological systems arise from rich internal state dynamics that allow them to adjust behavior in response to environmental cues. While active matter is widely used to model such systems, most existing frameworks ignore these internal mechanisms and rely on prescribed forces or rules, without capturing how energy is transduced. Here, we introduce a...
The manipulation of material properties by laser light holds great promise for the development of future technologies. However, the full picture of the electronic response to laser driving remains to be uncovered. We present an approach to reveal details of the electron dynamics of laser-dressed materials, which consists of calculating and analysing the time-dependent partial...
Laguerre-Gaussian (LG) vortex beams with non-zero radial indices provide an additional spatial degree of freedom beyond conventional azimuthal-order vortices, yet their generation in the femtosecond regime has remained experimentally challenging. Here, we report the direct generation of femtosecond radial-mode LG vortex pulses enabled by a mode-locked solid-state oscillator...
Charge-to-orbital conversion via the orbital Rashba-Edelstein effect represents a key functionality for orbitronics but has been challenging to identify. Here, we combine first-principles density functional theory, linear-response theory, and magneto-optical modeling to reveal how this effect can be detected optically through the quadratic magneto-optical Voigt effect in magnetic...
Predicting superconducting properties from first principles—especially in non-equilibrium conditions—is computationally intensive. Here, we propose a more efficient approach by using the electron localization function (ELF) as a proxy for estimating the superconducting critical temperature TC. Through first-principles calculations, we investigate how coupling conventional...
Random lasers rely on multiple scattering in disordered media to generate emission with complex spectral behavior. While their high-entropy output is valuable for random number generation, the inherent unpredictability has historically limited their utility in structured information processing. Here, we demonstrate the coexistence of spectral randomness and determinism within...
The measurement of tunneling times in strong-field ionization has been the topic of much controversy in recent years, with the attoclock and Larmor clock being two of the main contenders for correctly reproducing these times. While the non-zero Larmor tunneling time has been unambiguously detected, the tunneling time measured by the attoclock appears to vanish in tunnel...
Wave-topology interactions underlie phenomena across physics, from condensed matter to cosmology, with the Aharonov-Bohm (AB) effect providing a paradigmatic example. Classical analogues in fluid dynamics have shown that travelling surface waves scattered off vortices develop wavefront dislocations, with topological effects studied close to the vortex core. Here, we demonstrate...
In simulations of colloidal matter, frictional contacts between particles are often neglected. For spherical colloids, such an approximation can be problematic, since frictional contacts couple translational and rotational degrees of freedom, which may affect the collective behavior of, e.g., colloids under shear and chiral active matter. Deterministic models for frictional...
Accurate modeling of atomic masses with reliable uncertainty quantification is essential for understanding heavy-element production in astrophysical environments. This remains challenging because uncertainties arise not only from model parameters but also from structural limitations, often leading to underestimation when extrapolating beyond known nuclei. Here, we introduce SPICE...
From oil drums to flying rockets, cylindrical shells are valued for their load-carrying capacity. When sufficiently compressed, they buckle, with the phenomenon taking many forms, from periodic diamond-shaped buckles to localized elephant footing. The precise physical mechanisms of buckling are different, for example, in empty shells and shells with a solid core. However, despite...
Sensitivity to small changes in the environment is crucial for many real-world tasks, enabling living and artificial systems to make correct behavioral decisions. It has been shown that such sensitivity is maximized when a system operates near the critical point of a phase transition. However, proximity to criticality introduces large fluctuations and diverging timescales. Hence...
Dark matter constitutes approximately 85% of the universe’s total mass, yet its nature remains a mystery. Beyond its gravitational effects, little is known about its fundamental properties. Various technologies sensitive to different types of dark matter particles have been developed to explore the vast parameter space and cross-check each other’s results. In this perspective, we...
Microfluidics experiments offer high-resolution insights into transport and chemical processes in porous media, yet direct measurement of evolving concentration profiles remains challenging. Numerical simulations can serve as virtual probes but are labor-intensive and computationally expensive. Here, we develop a physics-based machine learning toolbox that transforms such...