Ultrastrong light-matter coupling has traditionally been studied in optical cavities, where it occurs when the light-matter coupling strength reaches a significant fraction of the transition frequency. This regime fundamentally alters the ground and excited states of the particle-cavity system, unlocking new ways to control its physics and chemistry. However, achieving ultrastrong coupling in engineered cavities remains a major challenge. Here, we show that ultra- and deep-strong coupling naturally occur in bulk materials without the need for external cavities. By analyzing experimental data from over 70 materials, we demonstrate that phonon-, exciton-, and plasmon-polaritons in many solids exhibit ultrastrong coupling, systematically surpassing the coupling strengths achieved in cavity-based systems. To explain this phenomenon, we introduce a dipole lattice model based on a generalized Hopfield Hamiltonian, which unifies photon-matter, matter-matter, and photon-photon interactions. The complete overlap between the photonic and collective dipole modes in the lattice enables ultrastrong coupling, leading to excited-state mixing, radiative decay suppression, and potential phase transitions into collective ground states. Applying our model to real materials, we show that it reproduces light-matter coupling across broad material classes and may underlie structural phase transitions that give rise to emergent phenomena such as ferroelectricity, insulator-to-metal transitions, and exciton condensation. Recognizing ultrastrong coupling as an intrinsic property of solids reshapes our understanding of light-matter interactions and opens new avenues for exploring quantum materials and exotic phases of matter.

Materials databases built from calculations based on density functional approximations play an important role in the discovery of materials with improved properties. Most databases thus constructed rely on the generalized gradient approximation (GGA) for electron exchange and correlation. This limits the reliability of these databases, as well as the artificial intelligence (AI) models trained on them, for certain classes of materials and properties which are not well described by GGA. In this paper, we describe a database of 7,024 inorganic materials presenting diverse structures and compositions generated using hybrid functional calculations enabled by their efficient implementation in the all-electron code FHI-aims. The database is used to evaluate the thermodynamic and electrochemical stability of oxides relevant to catalysis and energy related applications. We illustrate how the database can be used to train AI models for material properties using the sure-independence screening and sparsifying operator (SISSO) approach.

For solid-state materials, the electronic structure, E(k), is critical in determining a crystal's physical properties. By experimentally detecting the electronic structure, the fundamental physics can be revealed. Angle-resolved photoemission spectroscopy (ARPES) is a powerful technique for directly observing the electronic structure with energy- and momentum-resolved information. Over the past decades, major improvements in the energy and momentum resolution, alongside the extension of ARPES observables to spin (SpinARPES), micrometer or nanometer lateral dimensions (MicroARPES/NanoARPES), and femtosecond timescales (TrARPES), have led to major scientific advances. These advantages have been achieved across a wide range of quantum materials, such as high-temperature superconductors, topological materials, two-dimensional materials and heterostructures. This primer introduces key aspects of ARPES principles, instrumentation, data analysis, and representative scientific cases to demonstrate the power of the method. Perspectives and challenges on future developments are also discussed.

Control and detection of spin order in ferromagnets is the main principle allowing storing and reading of magnetic information in nowadays technology. The large class of antiferromagnets, on the other hand, is less utilized, despite its very appealing features for spintronics applications. For instance, the absence of net magnetization and stray fields eliminates crosstalk between neighbouring devices and the absence of a primary macroscopic magnetization makes spin manipulation in antiferromagnets inherently faster than in ferromagnets. However, control of spins in antiferromagnets requires exceedingly high magnetic fields, and antiferromagnetic order cannot be detected with conventional magnetometry. Here we provide an overview and illustrative examples of how electromagnetic radiation can be used for probing and modification of the magnetic order in antiferromagnets. Spin pumping from antiferromagnets, propagation of terahertz spin excitations, and tracing the reversal of the antiferromagnetic and ferroelectric order parameter in multiferroics are anticipated to be among the main topics defining the future of this field.

Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision. Among the existing approaches based on nonlinear histogram transformations, contrast limited adaptive histogram equalization (CLAHE) is a popular choice for dealing with 2D images obtained in natural and scientific settings. The recent hardware upgrade in data acquisition systems results in significant increase in data complexity, including their sizes and dimensions. Measurements of densely sampled data higher than three dimensions, usually composed of 3D data as a function of external parameters, are becoming commonplace in various applications in the natural sciences and engineering. The initial understanding of these complex multidimensional datasets often requires human intervention through visual examination, which may be hampered by the varying levels of contrast permeating through the dimensions. We show both qualitatively and quantitatively that using our multidimensional extension of CLAHE (MCLAHE) simultaneously on all dimensions of the datasets allows better visualization and discernment of multidimensional image features, as demonstrated using cases from 4D photoemission spectroscopy and fluorescence microscopy. Our implementation of multidimensional CLAHE in Tensorflow is publicly accessible and supports parallelization with multiple CPUs and various other hardware accelerators, including GPUs.

The discrete rotational symmetry of crystals leads to the conservation of quantized angular momentum in solids. While the exchange of energy and linear momentum between lattice vibrations (phonons) via anharmonic coupling is a cornerstone of solid-state physics, conservation and transfer of angular momentum within the lattice remains a postulate. Recently, phonon angular momentum, often in the form of chiral phonons, has been linked to giant magnetic fields, thermal Hall conductivity, dynamical multiferroicity, ultrafast demagnetization, or magnetic switching. However, the inherent process of phonon to phonon angular momentum transfer, fundamentally required to reach any magnetization equilibrium and imperative for all spin relaxation phenomena in solids, remains elusive. Here, we demonstrate the coherent transfer of angular momentum from one lattice mode to another by establishing helical nonlinear phononics. We directly observe rotational phonon-phonon Umklapp scattering dictated by pseudo angular momentum conservation and the threefold rotational symmetry of the topological insulator bismuth selenide. We identify nonlinear phonon-phonon coupling as an angular momentum transfer channel, confirmed by our ab-initio calculations. Besides bearing universal implications for angular momentum dissipation abundant in nature, our work actively reverses the natural flow, leading to the nonlinear upconversion of lattice angular momentum. We thus open the field of helical and chiral nonlinear phononics, representing a selective handle for ultrafast control of spins, topology and chiral quasi-particles.

The exploration of ultrafast phenomena is a frontier of condensed matter research, where the interplay of theory, computation, and experiment is unveiling new opportunities for understanding and engineering quantum materials. With the advent of advanced experimental techniques and computational tools, it has become possible to probe and manipulate nonequilibrium processes at unprecedented temporal and spatial resolutions, providing insights into the dynamical behavior of matter under extreme conditions. These capabilities have the potential to revolutionize fields ranging from optoelectronics and quantum information to catalysis and energy storage. This Roadmap captures the collective progress and vision of leading researchers, addressing challenges and opportunities across key areas of ultrafast science. Contributions in this Roadmap span the development of ab initio methods for time-resolved spectroscopy, the dynamics of driven correlated systems, the engineering of materials in optical cavities, and the adoption of FAIR principles for data sharing and analysis. Together, these efforts highlight the interdisciplinary nature of ultrafast research and its reliance on cutting-edge methodologies, including quantum electrodynamical density-functional theory, correlated electronic structure methods, nonequilibrium Green's function approaches, quantum and ab initio simulations.

Lead halide perovskites (LHPs) have emerged as an excellent class of semiconductors for next-generation solar cells and optoelectronic devices. Tailoring physical properties by fine-tuning the lattice structures has been explored in these materials by chemical composition or morphology. Nevertheless, its dynamic counterpart, phonon-driven ultrafast material control, as contemporarily harnessed for oxide perovskites, has not been established yet. Here we employ intense THz electric fields to obtain direct lattice control via nonlinear excitation of coherent octahedral twist modes in hybrid CH3NH3PbBr3 and all-inorganic CsPbBr3 perovskites. These Raman-active phonons at 0.9 - 1.3 THz are found to govern the ultrafast THz-induced Kerr effect in the low-temperature orthorhombic phase and thus dominate the phonon-modulated polarizability with potential implications for dynamic charge carrier screening beyond the Froehlich polaron. Our work opens the door to selective control of LHP's vibrational degrees of freedom governing phase transitions and dynamic disorder.

The topology of a power grid is estimated using an information theoretic approach. By modeling the grid as a graph and using voltage magnitude data of individual nodes in the grid, the mutual information between pairs of nodes is computed using different approximation methods. Using the well-known Chow-Liu algorithm, a maximum spanning tree based on mutual information is computed to estimate the power grid topology. Experiments and results are presented to optimize this approach with success shown for IEEE networks generated with MATPOWER and data generated using GridLAB-D. The algorithm is then cross-validated on IEEE networks generated by the European Union Joint Research Council.

Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmark datasets. Here, we investigate the performance of machine learning with kernel ridge regression (KRR) for the prediction of molecular orbital energies on three large datasets: the standard QM9 small organic molecules set, amino acid and dipeptide conformers, and organic crystal-forming molecules extracted from the Cambridge Structural Database. We focus on prediction of highest occupied molecular orbital (HOMO) energies, computed at density-functional level of theory. Two different representations that encode molecular structure are compared: the Coulomb matrix (CM) and the many-body tensor representation (MBTR). We find that KRR performance depends significantly on the chemistry of the underlying dataset and that the MBTR is superior to the CM, predicting HOMO energies with a mean absolute error as low as 0.09 eV. To demonstrate the power of our machine learning method, we apply our model to structures of 10k previously unseen molecules. We gain instant energy predictions that allow us to identify interesting molecules for future applications.

The interplay between Anderson localization and Coulomb repulsion reveals deep connections to superconductivity and many-body localization in quantum systems. In this study, we investigate a tin monolayer on silicon, a material known for its Mott and antiferromagnetic behavior, as it undergoes a metal-insulator transition near a band edge. By analyzing spatial correlations of the local density of states (LDOS) with scanning tunneling spectroscopy, we precisely identify the mobility edge and determine its critical exponent as nu = 0.75 +/- 0.1. Our findings show that both LDOS distribution functions and multifractal spectra obey two exact symmetry relations based on the Weyl group symmetry of nonlinear sigma-models. These symmetries hold across the entire transition, from extended to strongly localized states, in agreement with theoretical predictions. Additionally, we observe a power-law scaling of LDOS fluctuations close to the band edge (power -1.7), deep in the localized regime. Using tight-binding models, we demonstrate that breaking time-reversal symmetry significantly improves the agreement between our experimental data and theory compared to the standard Anderson model. Overall, our results provide a unifying framework for understanding localization at the band gap edge of a strongly correlated 2D material and show how localization patterns reflect the symmetry class of disordered electronic systems.

Ferroelectric materials contain a switchable spontaneous polarization that persists even in the absence of an external electric field. The coexistence of ferroelectricity and metallicity in a material appears to be illusive, since polarization is ill-defined in metals, where the itinerant electrons are expected to screen the long-range dipole interactions necessary for dipole ordering. The surprising discovery of the polar metal, LiOsO3 has generated interest in searching for new polar metals motivated by the prospects of exotic quantum phenomena such as unconventional pairing mechanisms giving rise to superconductivity, topological spin currents, anisotropic upper critical fields, and Mott multiferroics. Previous studies have suggested that the coordination preferences of the Li atom play a key role in stabilizing the polar metal phase of LiOsO3, but a thorough understanding of how polar order and metallicity can coexist remains elusive. Here, we use XUV-SHG as novel technique to directly probe the broken inversion-symmetry around the Li atom. Our results agree with previous theories that the primary structural distortion that gives rise to the polar metal phase in LiOsO3 is a consequence of a sub-Angstrom Li atom displacement along the polar axis. A remarkable agreement between our experimental results and ab initio calculations provide physical insights for connecting the nonlinear response to unit-cell spatial asymmetries. It is shown that XUV-SHG can selectively probe inversion-breaking symmetry in a bulk material with elemental specificity. Compared to optical SHG methods, XUV-SHG fills a key gap for studying structural asymmetries when the structural distortion is energetically separated from the Fermi surface. Further, these results pave the way for time-resolved probing of symmetry-breaking structural phase transitions on femtosecond timescales with element specificity.

Orthorhombic HoMnO3 is a multiferroic in which Mn antiferromagnetic order induces ferroelectricity. A second transition occurs within the multiferroic phase, in which a strong enhancement of the ferroelectric polarization occurs concomitantly to antiferromagnetic ordering of Ho 4f magnetic moments. Using the element selectivity of resonant X-ray diffraction, we study the magnetic order of the Mn 3d and Ho 4f moments. We explicitly show that the Mn magnetic order is affected by the Ho 4f magnetic ordering transition. Based on the azimuthal dependence of the (0 q 0) and (0 1-q 0) magnetic reflections, we suggest that the Ho 4f order is similar to that previously observed for Tb 4f in TbMnO3, which resembles an ac-cycloid. This is unlike the Mn order, which has already been shown to be different for the two materials. Using non-resonant diffraction, we show that the magnetically-induced ferroelectric lattice distortion is unaffected by the Ho ordering, suggesting a mechanism through which the Ho order affects polarization without affecting the lattice in the same manner as the Mn order.

We address the double hydrogen transfer (DHT) dynamics of the porphycene molecule: A complex paradigmatic system where the making and breaking of H-bonds in a highly anharmonic potential energy surface requires a quantum mechanical treatment not only of the electrons, but also of the nuclei. We combine density-functional theory calculations, employing hybrid functionals and van der Waals corrections, with recently proposed and optimized path-integral ring-polymer methods for the approximation of quantum vibrational spectra and reaction rates. Our full-dimensional ring-polymer instanton simulations show that below 100 K the concerted DHT tunneling pathway dominates, but between 100 K and 300 K there is a competition between concerted and stepwise pathways when nuclear quantum effects are included. We obtain ground-state reaction rates of $2.19 \times 10^{11} \mathrm{s}^{-1}$ at 150 K and $0.63 \times 10^{11} \mathrm{s}^{-1}$ at 100 K, in good agreement with experiment. We also reproduce the puzzling N-H stretching band of porphycene with very good accuracy from thermostatted ring-polymer molecular dynamics simulations. The position and lineshape of this peak, centered at around 2600 cm$^{-1}$ and spanning 750 cm$^{-1}$, stems from a combination of very strong H-bonds, the coupling to low-frequency modes, and the access to $cis$-like isomeric conformations, which cannot be appropriately captured with classical-nuclei dynamics. These results verify the appropriateness of our general theoretical approach and provide a framework for a deeper physical understanding of hydrogen transfer dynamics in complex systems.

Information and data exchange is an important aspect of scientific progress. In computational materials science, a prerequisite for smooth data exchange is standardization, which means using agreed conventions for, e.g., units, zero base lines, and file formats. There are two main strategies to achieve this goal. One accepts the heterogeneous nature of the community which comprises scientists from physics, chemistry, bio-physics, and materials science, by complying with the diverse ecosystem of computer codes and thus develops "converters" for the input and output files of all important codes. These converters then translate the data of all important codes into a standardized, code-independent format. The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs, outputs, and restart files, directly into the same code-independent format. We like to emphasize in this paper that these two strategies can and should be regarded as complementary, if not even synergetic. The main concepts and software developments of both strategies are very much identical, and, obviously, both approaches should give the same final result. In this paper, we present the appropriate format and conventions that were agreed upon by two teams, the Electronic Structure Library (ESL) of CECAM and the NOMAD (NOvel MAterials Discovery) Laboratory, a European Centre of Excellence (CoE). This discussion includes also the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations.

The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on the sure-independence screening and sparsifying operator (SISSO) symbolic-regression approach. SISSO identifies the few, key parameters correlated with a given materials property via analytical expressions, out of many offered primary features. Crucially, we train ensembles of SISSO models in order to quantify mean predictions and their uncertainty, enabling the use of SISSO in AL. By combining bootstrap sampling to obtain training datasets with Monte-Carlo feature dropout, the high prediction errors observed by a single SISSO model are improved. Besides, the feature dropout procedure alleviates the overconfidence issues observed in the widely used bagging approach. We demonstrate the SISSO-guided AL workflow by identifying acid-stable oxides for water splitting using high-quality DFT-HSE06 calculations. From a pool of 1470 materials, 12 acid-stable materials are identified in only 30 AL iterations. The materials property maps provided by SISSO along with the uncertainty estimates reduce the risk of missing promising portions of the materials space that were overlooked in the initial, possibly biased dataset.

We present the Novel-Materials-Discovery (NOMAD) Artificial-Intelligence (AI) Toolkit, a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable, accessible, interoperable, and reusable (FAIR) data. The AI Toolkit readily operates on the FAIR data stored in the central server of the NOMAD Archive, the largest database of materials-science data worldwide, as well as locally stored, users' owned data. The NOMAD Oasis, a local, stand alone server can be also used to run the AI Toolkit. By using Jupyter notebooks that run in a web-browser, the NOMAD data can be queried and accessed; data mining, machine learning, and other AI techniques can be then applied to analyse them. This infrastructure brings the concept of reproducibility in materials science to the next level, by allowing researchers to share not only the data contributing to their scientific publications, but also all the developed methods and analytics tools. Besides reproducing published results, users of the NOMAD AI toolkit can modify the Jupyter notebooks towards their own research work.

Polaritons are a hybrid class of quasiparticles originating from the strong and resonant coupling between light and matter excitations. Recent years have witnessed a surge of interest in novel polariton types, arising from directional, long-lived material resonances, and leading to extreme optical anisotropy that enables novel regimes of nanoscale, highly confined light propagation. While such exotic propagation features may also be in principle achieved using carefully designed metamaterials, it has been recently realized that they can naturally emerge when coupling infrared light to directional lattice vibrations, i.e., phonons, in polar crystals. Interestingly, a reduction in crystal symmetry increases the directionality of optical phonons and the resulting anisotropy of the response, which in turn enables new polaritonic phenomena, such as hyperbolic polaritons with highly directional propagation, ghost polaritons with complex-valued wave vectors, and shear polaritons with strongly asymmetric propagation features. In this Review, we develop a critical overview of recent advances in the discovery of phonon polaritons in low-symmetry crystals, highlighting the role of broken symmetries in dictating the polariton response and associated nanoscale-light propagation features. We also discuss emerging opportunities for polaritons in lower-symmetry materials and metamaterials, with connections to topological physics and the possibility of leveraging anisotropic nonlinearities and optical pumping to further control their nanoscale response.

A central prospect of antiferromagnetic spintronics is to exploit magnetic properties that are unavailable with ferromagnets. However, this poses the challenge of accessing such properties for readout and control. To this end, light-induced manipulation of the transient ground state, e.g. by changing the magnetic anisotropy potential, opens promising pathways towards ultrafast deterministic control of antiferromagnetism. Here, we use this approach to trigger a $\it{coherent}$ rotation of the entire long-range antiferromagnetic spin arrangement about a crystalline axis in $GdRh_2Si_2$ and demonstrate $\it{deterministic}$ control of this rotation upon ultrafast optical excitation. Our observations can be explained by a displacive excitation of the Gd spins$'$ local anisotropy potential by the optical excitation, allowing for a full description of this transient magnetic anisotropy potential.

Charge transport in organic semiconductors is limited by dynamical disorder. Design rules for new high-mobility materials have therefore focused on limiting its two foundations: structural fluctuations and the transfer integral gradient. However, it has remained unclear how these goals should be translated into molecular structures. Here we show that a specific shape of the frontier orbital, with a lack of nodes along the long molecular axis, reduces the transfer integral gradient and therefore the dynamical disorder. We investigated single crystals of the prototypical molecular semiconductors pentacene and picene by angle-resolved photoemission spectroscopy and dynamical disorder calculations. We found that picene exhibits a remarkably low dynamical disorder. By separating in- and out-of-plane components of dynamical disorder, we identify the reason as a reduced out-of-plane disorder from a small transfer integral derivative. Our results demonstrate that molecules with an armchair $\pi$-electron topology and same-phase frontier orbitals like picene are promising molecular building blocks for the next generation of organic semiconductors.