Advanced nuclear energy systems critically rely on the irradiation tolerance of structural materials in the reactor core. However, quantitatively predicting reactor-relevant, high-dose neutron swelling from low-dose neutron data or high-dose-rate heavy-ion irradiation experiments remains a long-standing challenge. Existing approaches are often case-specific, requiring neutron–ion benchmarking and substantial empirical tuning. Here we derive a quantitative, physically grounded relationship linking cavity swelling to both dose and dose rate at a fixed temperature, combining cluster-dynamics insights—particularly dose-rate-enhanced defect nucleation—with simplified rate-equation analysis. Validation across extensive literature data demonstrates that the proposed relationship is broadly applicable across major classes of structural alloys and irradiation conditions, accurately capturing the nonlinear dose dependence of swelling and its systematic variation with dose rate. It thereby enables the prediction of high-dose swelling behavior from low-dose data, defines an equivalent dose that produces the same swelling level across different dose rates, and provides a quantitative basis for comparing swelling resistance across materials, independent of specific material systems or irradiation conditions. Furthermore, ion-irradiation experiments demonstrate that, using ion data alone, the relationship enables quantitative prediction of neutron-irradiation swelling across an ion–neutron dose-rate gap spanning several orders of magnitude. This work provides a practical and predictive framework for evaluating irradiation-induced swelling in structural materials, offering a quantitative pathway to bridge ion and neutron irradiation experiments at high irradiation doses and supporting materials development and safety evaluation for advanced nuclear energy systems.

Selenium, the world’s oldest photovoltaic material, has experienced a renaissance in research over the past decade, with certified solar cell efficiencies climbing from the historical record of 5% to breaking the 10% barrier. Its wide bandgap makes it a particularly interesting candidate for tandem solar cells and indoor photovoltaic applications, yet despite steadily improving the carrier collection, devices consistently suffer from a substantial open-circuit voltage deficit. This review provides a critical analysis of the material properties and optoelectronic quality of state-of-the-art selenium thin films. Published results from independent groups are digitized and directly compared, collectively painting a comprehensive picture of the carrier dynamics, supported and contextualized by drift-diffusion simulations. Strategies for synthesizing and processing selenium thin films are also examined in detail, highlighting not only best practices but also the underlying crystal growth kinetics that ultimately govern material quality. Finally, a series of open questions and challenges is presented, spanning from fundamental materials science and atomic-scale defect physics to device-level engineering, providing a roadmap to unlock the intrinsic photovoltaic potential of selenium and guide the future development of higher-efficiency selenium solar cells.

We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode material for grid-scale sodium-ion batteries, our methodology employs an active-learning strategy to train a moment tensor potential through iterative hybrid grand-canonical Monte Carlo-molecular dynamics sampling, robustly capturing configuration spaces at all sodiation levels. The resulting machine learning interatomic potential accurately reproduces experimental properties, including volume expansion, operating voltage, and sodium concentration-dependent structural transformations, while revealing a four-order-of-magnitude difference in sodium diffusivity between the rhombohedral (sodium-rich) and tetragonal (sodium-poor) phases at 300 K. We directly compute all critical parameters—temperature- and concentration-dependent diffusivities, interfacial and strain energies, and complete free-energy landscapes—to feed them into pseudo-2D phase-field simulations that predict phase-boundary propagation and rate-dependent performances across electrode length scales. This multiscale workflow establishes a blueprint for rational computational design of next-generation insertion-type materials, such as battery electrode materials, demonstrating how atomistic insights can be systematically translated into continuum-scale predictions.

We present a performance modeling and degradation analysis framework for tandem photovoltaic modules, building upon established procedures for crystalline silicon devices and adapting them to account for the spectral sensitivity of multijunction technologies. The methodology employs filter criteria to select outdoor measurements close to standard test conditions (STC) under stable spectral and ambient conditions, followed by normalization of power production data with corrections for temperature, irradiance, and precipitable water vapor. We demonstrate this framework using a mechanically stacked four-terminal gallium arsenide ($\mathrm{Ga}\mathrm{As}$) // silicon ($\mathrm{Si}$) tandem solar minimodule deployed outdoors from October 2019 to January 2021 in Golden, Colorado, USA. We determined degradation rates of $-4.1\pm 0.2\mathrm{\%}/$year for the $\mathrm{Ga}\mathrm{As}$ subcell and $-2.5\pm 0.9\mathrm{\%}/$year for the $\mathrm{Si}$ subcell, with analysis of individual performance metrics indicating that packaging degradation, particularly delamination, was the dominant failure mode. Simulations using PVcircuit, an open-source equivalent-circuit solver, confirmed these findings. The presented methodology provides a reproducible foundation for performance modeling and degradation analysis of emerging tandem technologies.

Understanding thermal transport in ${\mathrm{Al}}_x{\mathrm{Ga}}_{1-x}\mathrm{N}$ alloys is critical for the reliable thermal management of high-power electronic devices. However, theoretical predictions of lattice thermal conductivity ${\kappa }_{\ell }$ based on the traditional Abele’s virtual crystal approximation (VCA) significantly deviate from experimental measurements, and these discrepancies have long been attributed to finite-size effects. Unlike the traditional VCA, which homogenizes atomic masses and bonding environments, we explicitly incorporate both mass disorder and interatomic force constant (IFC) disorder by directly performing Boltzmann transport equation calculations on large special quasirandom structures, aiming to uncover their impact on lattice thermal transport. Our results show that mass disorder plays a dominant role in suppressing ${\kappa }_{\ell }$ by significantly enhancing the anharmonic scattering of low-frequency phonons, whereas the heat-carrying capacity of high-frequency phonons is completely eliminated due to substantially reduced group velocities and strong harmonic scattering. In comparison, IFC disorder mainly increases anharmonic scattering rates but has negligible influence on the group velocities of low-frequency phonons. When both disorder types are accounted for, the predicted thermal conductivities align closely with experimental observations. This indicates that the previously reported overestimations from VCA are not due to finite-size effects. We also find that the alloy scattering model based on perturbation theory significantly overestimates the harmonic scattering rates of high-frequency phonons, despite their negligible contribution to thermal conductivity. These findings highlight the necessity of atomistic disorder modeling in wide-bandgap alloys and provide critical insights for thermal design in ${\mathrm{Al}}_x{\mathrm{Ga}}_{1-x}\mathrm{N}$-based electronics.

Wind-farm power spectra display distinct regimes that reflect how turbines collectively interact with the atmosphere. At low frequencies (approximately ${10}^{-4}$ Hz and below), turbine power outputs remain correlated, whereas at high frequencies (approximately ${10}^{-2}$ Hz and above), they decorrelate, smoothing wind-farm power fluctuations. Existing models typically capture only one end of this spectrum and lack a unified description across the transitional range (approximately ${10}^{-3}$ Hz). Here we introduce a dynamic sweeping model that predicts wind-farm power spectra across all scales. Slow mesoscale variations are represented as a time-dependent sweeping velocity that advects boundary-layer turbulence through the wind farm, while small-scale turbulence is modeled with use of the random sweeping framework. The model shows that the three spectral regimes originate from interactions between mesoscale weather systems, with timescales of about 15 min to hours, and microscale turbulence evolving over seconds to minutes. Large-eddy simulations and field data confirm that the model captures the mesoscale, transitional, and turbulence-dominated regimes, providing a physically consistent foundation for predicting wind power variability and supporting reliable grid integration.

Energy costs of information processing are growing exponentially. Bit erasure is a key problem in this energy-information nexus, and a number of seminal relationships have been deduced regarding the relationship between thermodynamic costs and memory storage. To continue making progress in the modern era, however, requires confronting thermodynamic costs in realistic physical systems, which operate away from equilibrium. Here, we explore the thermodynamic costs of bit erasure in a complementary metal-oxide-semiconductor model of two types of random-access memory. We find dynamic random-access memory dissipates the least amount of energy when operated in the quasistatic limit, where errors are also minimized. By contrast, static random-access memory is most efficiently operated in finite time due to the energy required to maintain the state of the bit. We demonstrate a numerically robust optimization scheme using mean field theory and automatic differentiation, finding optimal protocols compatible with electrical engineering insights. These results provide a framework for operating realistic circuits in thermodynamically advantageous ways.

Graphite, the standard commercial anode for $\mathrm{Li}$-ion batteries, is thermodynamically incompatible with $\mathrm{Na}$-ion batteries, leading researchers to search for alternative C-based structures (e.g., hard carbon, expanded graphite). In a simplified picture, the main idea of such a search relies on identifying disordered C structures with a large interlayer spacing and distribution of local structural motifs (e.g., pores) with target electrochemical properties. Such an exploration is typically done via trial-and-error experimentation, which often does not allow precise understanding of the role of interlayer distance and even $\mathrm{Na}\text{−}\mathrm{or}$ $\mathrm{Li}$-ion intercalation in the electrochemical performance. Motivated by this, using density-functional theory and cluster expansion, we establish a structure-property relationship for $\mathrm{Li}$ and $\mathrm{Na}$ intercalation across a range of graphite interlayer spacings and stacking arrangements. We show that $\mathrm{Na}$ intercalation becomes thermodynamically possible at high concentrations once the interlayer spacing exceeds 4.21 Å even without a change in interlayer spacing. Conversely, $\mathrm{Li}$ intercalation has a narrow optimal window, with maximum capacity (close to the commercial limit) at approximately 3.75 Å, while larger spacings (e.g., 4.58 Å) quickly reduce $\mathrm{Li}$ storage capacity. We also find that AA-stacked domains consistently offer stronger ion bonding and higher voltages than AB-stacked domains for both ions. Our results, thus, not only explain the role of metal-ion intercalation in the electrochemical performance, but also clarify the fundamental design trade-offs for expanded C anodes, offering practical targets and interlayer distance ranges for the independent optimization of the next generation of negative electrodes for metal-ion batteries.

Precise control over electronic coupling at nanoscale interfaces is critical for designing materials with tunable charge-transfer behavior and catalytic function. Heterostructures with locally coherent interfaces provide a platform for interrogating interfacial charge redistribution in coupled material systems. Here, we report ${\mathrm{Cu}}_2\mathrm{S}/{\mathrm{Mo}\mathrm{S}}_2$ heterostructures exhibiting nanoscale crystallographic alignment, which are synthesized through a rapid thermal transformation pathway. We employed electrochemical reduction reactions to probe interfacial charge transfer, revealing shifts in product distribution attributable to modified interfacial energetics, even in the absence of optimized catalytic performance. The observed formate Faradaic efficiency suggests that interfacial electronic modulation in the heterostructure shifts product selectivity towards formate, highlighting how interface-driven electronic modulation can direct reaction pathways and influence product selectivity. Optimizing catalyst loading, architecture, and reactor configuration will be critical for future improvement. Structural and compositional integrity were confirmed through powder x-ray diffraction, x-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy. Electron transfer between ${\mathrm{Cu}}_2\mathrm{S}$ and ${\mathrm{Mo}\mathrm{S}}_2$ domains was further evaluated by electrochemical impedance spectroscopy, while selected-area electron diffraction revealed local crystallographic alignment consistent with a local epitaxial relationship at the ${\mathrm{Cu}}_2\mathrm{S}/{\mathrm{Mo}\mathrm{S}}_2$ heterointerface. To illustrate the broader applicability of this approach, a $\mathrm{Zn}\mathrm{S}/{\mathrm{Mo}\mathrm{S}}_2$ heterostructure was also synthesized using the same microwave strategy, confirming the generalizability of interfacial engineering principles across metal sulfide-${\mathrm{Mo}\mathrm{S}}_2$ systems. Collectively, these findings demonstrate that controlled local epitaxial alignment serves as an effective design principle for tuning interfacial energetics and catalytic reactivity in complex heterostructure materials.

Polymerized Y-type nonfullerene acceptors (PY-NFAs) have shown great promise for efficient and stable organic solar cells (OSCs), yet their excited-state dynamics and structure-photophysics-performance relationships remain unclear. Here, we investigate a series of PY-NFAs and distinguish between intrachain excitons (intra-Ex) and interchain excitons (inter-Ex) formed in the solid state using cryogenic transient absorption (TA) spectroscopy at 100 K. We identify the emergence of trapped intra-Ex in the PY-NFA films from approximately 100 ps onwards after photoexcitation. By comparing this low-temperature TA signal among different PY-NFA films, we uncover a strong correlation between the trapped intra-Ex signature and the performance of these PY-NFAs in donor-acceptor blend OSC devices. This correlation indicates that the low-temperature TA response provides a quantitative measure of structural disorder in the PY-NFA aggregates and in the corresponding donor-acceptor blends. Correlating these spectroscopic results with molecular packing and morphological data, we reveal that high-performance PY-NFAs simultaneously exhibit (1) extended backbone conjugation and large intrachain exciton bandwidths and (2) uniform, oriented interchain packing despite moderately increased π-π stacking distances. These findings provide new design rules for PY-NFAs and offer deeper insight into how structural disorder and exciton dynamics govern OSC performance.

We describe the correlation structure of wind-power fluctuations in a farm of 80 turbines, sampled over 5 years. We report the presence of universal, collective, and nonlinear correlations, responsible for the excess persistency and intermittency of farm-aggregated power output. A first cross-correlation analysis of turbine production reveals a dynamical scaling transition (à la Family-Vicszek) from local decoherence to large-scale turbulence-driven scaling, and responsible for the geographical smoothing effect, previously reported beyond farm scale [M. M. Bandi, Spectrum of wind-power fluctuations, Phys. Rev. Lett. 118, 028301 (2017)]. A second bivariate analysis shows the long-range correlation of non-Gaussian features, responsible for their amplification in total farm output. These findings provide important insights into wind energy, the variability of which directly results from atmospheric turbulent forcing. By characterizing the multivariate and intermittent structure of fluctuations, our results advance the current understanding of wind-power variability and provide valuable priors for grid management and storage optimization, thereby supporting improved integration of wind energy into modern power systems.

We address the question of the optimal broadband absorption of waves in an open dissipative system and its key implications for solar cells. We develop a general framework for absorption induced by multiple overlapping resonances, based on quasinormal modes and radiative and nonradiative decay rates. We demonstrate that the upper bounds on broadband absorption in a slab of thickness $d$ take the simple form $A=1-\exp (-F\alpha d)$, where $\alpha$ is the absorption coefficient and $F$ is the path enhancement factor. We apply these results to sunlight absorption in photovoltaics and answer the long-standing debate on the best light-trapping strategy in solar cells. For angle-independent absorption enhanced by scattering, we derive the isotropic upper bound $F=4n^2$ (where $n$ is the refractive index), extending the well-known Lambertian (or Yablonovitch) limit beyond the ray-optics and weak-absorption regimes. For angle-restricted illumination, we show that $F$ can be further increased up to $8\pi n^2/\sqrt{3}$ using multiresonant absorption induced by periodical patterning. These results have a general scope in the field of wave physics and open new opportunities to maximize absorption, detection, and attenuation of electromagnetic or mechanical waves in ultrathin devices.

Temperature-dependent transport data, including diffusion coefficients and ionic conductivities, are routinely analyzed by fitting empirical models such as the Arrhenius equation. These fitted models yield parameters such as the activation energy, and can be used to extrapolate to temperatures outside the measured range. Researchers frequently face challenges in this analysis: quantifying the uncertainty of fitted parameters, assessing whether the data quality is sufficient to support a particular empirical model, and using these models to predict behavior at temperatures outside the measured range. Bayesian methods offer a coherent framework that addresses all of these challenges. This tutorial introduces the use of Bayesian methods for analyzing temperature-dependent transport data, covering parameter estimation, model selection, and extrapolation with uncertainty propagation, with illustrative examples from molecular dynamics simulations of superionic materials.

Lignin pyrolysis generates oxygenated aromatics that can serve as renewable biofuel precursors. Anisole and substituted anisoles are widely used as model compounds to probe processes related to their production and use. We present a comparative study of anisole and 4-vinylanisole thermal decomposition using vacuum-ultraviolet photoionization mass spectrometry in a silicon carbide microreactor from 300 to 1300 K. For both molecular species, the initial decomposition pathway is $\mathrm{O}—{\mathrm{CH}}_3$ bond cleavage. The vinyl substituent lowers the bond dissociation energy, shifting decomposition to lower temperatures for 4-vinylanisole. Anisole pyrolysis produces hydrogen-atom, methyl, propargyl, phenoxy, and cyclopentadienyl radicals, phenol, cyclopentadiene, cyclopentadienyl dimer, methylcyclopentadiene, $\mathrm{CO}$, acetylene, and benzene via methylcyclopentadiene. The products of 4-vinylanisole pyrolysis include hydrogen-atom, methyl, propargyl, fulvenallenyl, vinylphenoxy, vinylcyclopentadienyl, and cyclopentadienyl radicals, vinylphenol, vinylcyclopentadiene, dimethylfulvene, fulvenallene, vinylacetylene, $\mathrm{CO}$, and acetylene, but no benzene. Vinyl substitution thus facilitates resonance-stabilized radical (RSR) formation at temperatures lower than the cracking temperature of anisole. Whereas 4-vinylanisole decomposes at a lower temperature than anisole, both species produce significant RSR products that facilitate aromatic growth and gas-to-condensed-phase hydrocarbon conversion, in contrast to their methyl-substituted analog. These new insights into RSR formation are applicable to lignin pyrolysis and may be useful for limiting char or tar formation during biofuel production and for developing predictive models of emissions from smoldering and propagating flaming wildfires.

Ergotropy provides a fundamental measure of the extractable work from a quantum system and, consequently, of the maximal useful energy, or charge, stored within it. Understanding how this quantity can be manipulated and transformed efficiently is crucial for advancing quantum energy management technologies. Here, we introduce and formalize the concepts of isoergotropic states and ergotropy-preserving operations, which reorganize the internal structure of ergotropy while keeping its total value unchanged. These ideas are illustrated for both discrete (two-level systems) and continuous-variable systems (single-mode Gaussian states). In each case, we show how ergotropy-preserving operations redistribute the respective coherent-incoherent and displacement-squeezing components. We further examine the thermodynamic exchanges accompanying ergotropy-preserving operations, including variations in energy and entropy, and demonstrate that these transformations can be dynamically implemented through standard beam-splitter-type interactions with an auxiliary system. Finally, we discuss the practical implications of isoergotropic states and operations in optimizing charging protocols and mitigating charge loss in open quantum batteries.

Nonreciprocal quantum batteries (QBs) leverage engineered environments to impose unidirectional energy flow, achieving a fourfold enhancement in energy storage over conventional designs. In this work, we establish thermal-reservoir engineering as a powerful tool for controlling and optimizing the performance of QBs. We demonstrate that a thermal-reservoir-driven nonreciprocal QB achieves significantly enhanced charging, surpassing its vacuum-reservoir counterpart. Crucially, the nonreciprocal ergotropy is thermally invariant and coincides with the vacuum-reservoir energy, despite the temperature-dependent rise in total stored energy. This reveals a “thermally shielded” work potential, where nonreciprocity effectively decouples energy storage from energy quality, ensuring robust work extraction despite the thermal environment. Consequently, engineered thermal reservoirs emerge as a key resource for developing high-performance, noise-resilient quantum batteries.

Usable magnesium batteries are promising alternatives to lithium technology. The currently most widely used battery type based on lithium is reaching its cost-reduction limit due to the relative rarity of the precursors and environmental side effects. While other ions, such as sodium, have also been investigated for use in batteries, magnesium stands out due to its several advantages, including high abundance and high theoretical capacity. While most of the current magnesium batteries contain liquid electrolytes, solid-state electrolytes (SSEs) are especially of interest due to their potential advantages over the liquid ones, including enhanced safety, higher energy density, wider electrochemical stability window and operative temperature range, resistance to side reactions and degradation, and simplified battery-pack design. However, $\mathrm{Mg}$-based SSEs have their own difficulties, most notably the lack of ionic conductivity. Only a few $\mathrm{Mg}$-ion conductors can achieve superionic conductivity at room temperature with a low activation energy. Thus, researchers continually search for inorganic solids that can conduct $\mathrm{Mg}$ ions better while having no other issues, such as high electronic conductivity or electrochemical instability. With a wide range of materials that have already been investigated and a vast array with potential that remains untapped, it is essential to keep updated on this rapidly expanding field. This minireview aims to identify emerging $\mathrm{Mg}$-ion conductors that can serve as promising candidates for SSEs, as well as to review methods for enhancing the relevant properties of established materials. We highlight the new materials and methods investigated in the last 2–3 years that are yet to be covered in a review. An exciting new area of emerging research is highlighted, utilizing machine learning and high-throughput materials screening and calculations to identify promising inorganic $\mathrm{Mg}$-ion conductors from materials databases.

Methods of elucidating the mechanisms of fast-ion conduction in solid-state materials are pivotal for advancements in energy technologies such as batteries, fuel cells, sensors, and supercapacitors. In this study, we examine the ionic conduction pathways in single crystal ${\mathrm{Sr}\mathrm{Cl}}_2$, which is a fast-ion conductor above 900 K, using four-dimensional quasielastic neutron scattering (4D-QENS). We explore both coherent and incoherent neutron scattering at temperatures above the transition temperature into the superionic phase to explore the correlated motion of hopping anions. Refinements of the incoherent QENS yield residence times and jump probabilities between lattice sites in good agreement with previous studies, confirming that ionic hopping along nearest-neighbor directions is the most probable conduction pathway. However, the coherent QENS reveals evidence of de Gennes narrowing, indicating the importance of ionic correlations in the conduction mechanism. This highlights the need for improvements both in the theory of ionic transport in fluorite compounds and the modeling of coherent 4D-QENS in single crystals.

Halocarbons have a vast array of industrial applications and non-negligible environmental impact. Their structures, properties, and thermochemistry are often modeled with the use of density functional theory (DFT) calculations. Here we analyze the DFT-calculated gas-phase formation enthalpies of 56 halocarbons containing various amounts of $\mathrm{F}$, $\mathrm{Cl}$, $\mathrm{Br}$, and $\mathrm{I}$ using a group-additivity approach and seven exchange-correlation functionals (Perdew-Burke-Ernzerhof, Perdew-Wang 91, revised Perdew-Burke-Ernzerhof, Bayesian error estimation functional with van der Waals corrections, Tao-Perdew-Staroverov-Scuseria, Becke 3-parameter Lee-Yang-Parr, and Heyd-Scuseria-Ernzerhof 2006). Carbon—halogen bonds are found to introduce significant errors in the formation enthalpies, which are proportional to the electronegativity of the elements. We devised two correction methods based on the number and type of functional groups in each molecule in the reactants and products of the formation reactions. Both methods lower the mean absolute errors from approximately 0.35 eV to approximately 0.05 eV. Finally, the corrections were tested in an independent set of more than 150 reactions, and high accuracy was found without relying on error cancellation.

Mechanical and elastic properties of materials are among the most fundamental quantities for many engineering and industrial applications. Here, we present a formulation that is efficient and accurate for calculating the elastic and bending rigidity tensors of crystalline solids, leveraging interatomic force constants and long-wavelength perturbation theory. Crucially, in the long-wavelength limit, lattice vibrations induce macroscopic electric fields, which further couple with the propagation of elastic waves, and a separate treatment on the long-range electrostatic interactions is thereby required to obtain elastic properties under the appropriate electrical boundary conditions. A cluster expansion of the charge-density response and dielectric screening function in the long-wavelength limit has been developed to efficiently extract multipole and dielectric tensors of arbitrarily high order. We implement the proposed method in a first-principles framework and perform extensive validations on silicon, $\mathrm{Na}\mathrm{Cl}$, $\mathrm{Ga}\mathrm{As}$ and rhombohedral ${\mathrm{Ba}\mathrm{Ti}\mathrm{O}}_3$ as well as monolayer graphene, hexagonal $\mathrm{BN}$, ${\mathrm{Mo}\mathrm{S}}_2$, and $\mathrm{In}\mathrm{Se}$, obtaining good to excellent agreement with other theoretical approaches and experimental measurements. Notably, we establish that multipolar interactions up to at least octupoles are necessary to obtain the accurate short-circuit elastic tensor of bulk materials, while higher orders beyond octupole interactions are required to converge the bending rigidity tensor of two-dimensional crystals. The present approach greatly simplifies the calculations of bending rigidities and will enable the automated characterization of the mechanical properties of novel functional materials.