Developing metal halide perovskite (MHP) photovoltaic (PV) devices into reliable large-area solar modules could accelerate global solar energy deployment. Many MHP devices are susceptible to degradation under light and elevated temperature (LT). Published research on LT testing is limited at the module level, and LT testing has not yet been developed for qualification testing of commercial PV products. This report assesses whether results of LT testing at moderately elevated temperatures correlate with those of field-tested modules from the same batch. Six batches of samples from four manufacturers are assessed. It is shown that modules with a robust package that can maintain over 80% of their peak efficiency during LT testing at 55 °C for 100 h are more likely to retain over 80% of their peak efficiency during outdoor operation for 10 weeks. This finding is a step towards developing a validated test protocol that could be incorporated into a qualification standard for the commercialization of MHP PV technologies.

Reducing the dimensionality of metal-halide perovskites enhances quantum and dielectric confinement, enabling tunable excitonic properties. In one dimension, the arrangement of metal-halide octahedra in chains with corner-, edge-, or face-sharing connectivity allows for additional structural flexibility. This not only expands material design possibilities but also reflects quasi-one-dimensional motifs that arise during perovskite formation but are poorly understood. Using first-principles many-body perturbation theory within the $GW$ and Bethe-Salpeter equation framework, we provide a comprehensive picture of how one-dimensional confinement, octahedral connectivity and dielectric screening affect optical absorption and exciton photophysics in these materials. Our calculations reveal that increasing octahedral connectivity leads to increased exciton binding and complex, anisotropic optical signatures. However, in experimentally synthesized organic-inorganic systems, pronounced dielectric screening effects can reduce exciton binding energies by several hundred meV, altering these trends. These findings offer insights and design principles for excitonic properties, and aid the interpretation of optical experiments on one-dimensional perovskites.

Understanding time-resolved quantum transport is crucial for developing next-generation quantum technologies, particularly in nano- and molecular junctions subjected to time-dependent perturbations. Traditional steady-state approaches to quantum transport are not designed to capture the transient dynamics necessary for controlling electronic behavior at ultrafast timescales. In this work, we present a nonequilibrium Green’s function formalism, within the recently developed iterated generalized Kadanoff-Baym ansatz ($i\mathrm{GKBA}$), to study thermoelectric quantum transport beyond the wide-band limit approximation (WBLA). We employ the Meir-Wingreen formula for both charge and energy currents and analyze the transition from Lorentzian line-width functions to the WBLA, identifying unphysical divergences in the latter. Our results highlight the importance of finite-bandwidth effects and demonstrate the efficiency of the $i\mathrm{GKBA}$ approach in modeling time-resolved thermoelectric transport, also providing benchmark comparisons against the full Kadanoff-Baym theory. We exemplify the developed theory in the calculation of time-resolved thermopower and thermoelectric energy conversion efficiency in a cyclobutadiene molecular junction.

We present a continuum model for symmetry-breaking phase transformations in intercalation compounds, based on Ericksen’s multiwell energy formulation. The model predicts the nucleation and growth of crystallographic microstructures in ${\mathrm{Li}}_2{\mathrm{Mn}}_2{\mathrm{O}}_4$—a representative intercalation compound—with twin boundary orientations and volume fractions that closely match experimental observations. Our chemomechanically coupled model not only generates geometrically accurate microstructures through energy minimization, but also reveals a subtle interplay between twinned domains and electrochemomechanical behavior. A key finding is that intercalation compounds satisfying specific compatibility conditions (e.g., ${\lambda }_2=1$ or $|\det \mathbf{U}-1|=0$) show lower elastic energy barriers, require smaller driving forces, and display narrower voltage hysteresis loops. Furthermore, we show that twinned domains act as conduits for fast $\mathrm{Li}$ diffusion. These results establish quantitative design guidelines for intercalation compounds, which focuses on tailoring lattice deformations (rather than suppressing them) and reducing energy barriers to mitigate structural degradation and enhance the electrochemical performance of battery electrodes.

Understanding the fundamental limits of various energy conversion approaches is essential for assessing their efficiency and power output. In this work, we derive general performance bounds for thermoradiative heat engines that exchange heat radiatively with a cold sink, establishing power-versus-efficiency thermodynamic bounds for several configurations. We find that the performance of these engines is always bounded by that of radiative engines, which harness the thermal radiation emitted by a hot source, making thermoradiative engines inherently less favorable for energy conversion. By unifying the results of radiative and thermoradiative engines within a common thermodynamic framework, which also encompasses dual-engine configurations that combine both features, this work provides a comprehensive understanding of the thermodynamic limits of radiative energy conversion. Our framework offers general metrics for evaluating specific devices and raises critical questions regarding the relevance of thermoradiative cells for energy production.

Concerningly, numerous incorrect expressions for extracting phonon lifetimes from spectroscopic linewidths remain prevalent in the literature, resulting in misinterpretation of experimental data. This has significant consequences in the broader energy materials field, where erroneous lifetimes risk misinforming materials design and device modeling. We outline the origin of this ambiguity, provide historical context on how this error has propagated, and present the correct formula. In addition, we highlight a frequently overlooked source of error: the contribution of phase relaxation to spectral broadening. A unified approach enables fair comparison of values obtained from different spectroscopic methods, accurate benchmarking against first-principles calculations, and, most importantly, precise determination of macroscopic physical properties derived from phonon lifetime measurements.

In recent years, orientationally disordered crystals, or plastic crystals, have transformed the field of solid-state cooling due to the significant latent heat and entropy changes associated with their temperature-induced molecular order-disorder phase transition, which can produce colossal caloric effects under external field stimuli. However, the molecular mechanisms underlying these huge caloric effects remain inadequately understood and general principles for enhancing the performance of caloric plastic crystals are lacking. Previous studies have predominantly focused on molecular rotations, overlooking other potentially critical factors, such as lattice vibrations and molecular conformations. In this study, we employ classical molecular dynamics (MD) simulations to both replicate and elucidate the microscopic origins of the experimentally observed colossal barocaloric (BC) effects—those driven by hydrostatic pressure—in the archetypal plastic crystal neopentyl glycol (NPG). Our MD simulations demonstrate that in NPG, the combined BC response and phase-transition entropy changes arising from lattice vibrations and molecular conformations are nearly equal to those from molecular reorientations. These findings suggest that alongside hydrogen bonding—which directly impacts molecular rotational dynamics—lattice vibrational and molecular structural features, often overlooked, must be integrated into the rational design and modeling of advanced caloric plastic crystals. These insights are not only of significant fundamental interest but also essential for driving the development of next-generation solid-state refrigeration technologies.

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

The migration of mobile ions is one of the leading causes of the degradation of perovskite solar cells. However, quantifying mobile ions in complete perovskite solar cells is challenging due to the complex device stacks and the impact of charge transport layers on the measurement techniques. Here we develop a simple and openly accessible step model that approximates drift-diffusion simulations. The step model is based on the expression of the charge density in the ionic and electronic accumulation and depletion layers as step functions. We can then accurately determine the impact of mobile ions on the dc potential distribution of perovskite solar cells. Furthermore, we can simulate electrical measurement techniques commonly used to quantify mobile ions: capacitance transient, current transient, and capacitance-frequency measurements. By validating the step model with drift-diffusion simulations, we show that an accurate extraction of ion density, diffusion coefficient, and activation energy is possible in an accessible range. We finally apply the step model developed to estimate the ionic conductivity and activation energy of perovskite solar cells.

This study develops a high-fidelity multiphysics coupling framework to systematically investigate the impact of Chalk River unidentified deposits (CRUD) on pressurized-water-reactor (PWR) assemblies, integrating neutronics, thermal hydraulics, and chemical kinetics modeling. CRUD, formed via subcooled nucleate boiling (SNB) and corrosion-product deposition, alters local boron concentrations, thereby inducing a power shift (CIPS) due to CRUD and generating asymmetric axial power distributions. The proposed framework adopts an internally coupled approach, utilizing Picard iteration and a background-mesh technique to resolve multiscale computational challenges while maintaining numerical accuracy and efficiency. Numerical simulations of a $17\times 17$ PWR fuel assembly reveal that CRUD deposition reduces the eigenvalue by 293 percent mille (pcm) and shifts the axial power peak downward by 21 cm, corresponding to a 6.27% axial offset. Radially, the effect of CRUD on power distribution remains minimal (approximately 0.04%). A critical observation is the dominant influence of surface heat flux on CRUD growth, with thicker deposits and elevated boron concentrations observed in high-power regions. CRUD-induced perturbations also exhibit a “neighboring phenomenon,” wherein CRUD-free fuel rods experience comparable axial power shifts due to neutron-flux disturbances. These findings underscore the intricate coupling between power distribution, SNB, and CRUD chemistry, providing valuable insights for enhancing reactor performance and safety.

The p-i-n junction structure that develops dynamically under an applied bias via electrochemical doping (ECD) is of key importance for the performance of light-emitting electrochemical cells (LECs). While the complex electronic and ionic processes that govern its transient formation have been extensively studied by both experiments and drift-diffusion modeling, less attention has been given to the steady-state junction as a function of voltage. Here we study the formed p-i-n structure of a polymer LEC at the steady state by measuring and analyzing its most distinctive feature: the current density–voltage-luminance characteristics. Unexpectedly, we find that the effective conductance of the p-i-n structure exhibits a positive correlation with the applied bias, a behavior not predicted by existing LEC drift-diffusion models. We attribute this discrepancy to the assumption in these models of a constant density of mobile ions. Hence, we present a modified model in which the ECD level—represented by the number of ions with which the organic semiconductor is doped—scales with the applied voltage, implying a voltage-dependent doping efficiency. We validate this hypothesis using electron spin resonance spectroscopy and drift-diffusion modeling, while additionally establishing that only a small fraction of the available ions in our system, which increases from 1% to 3% with increasing bias, contributes to the ECD and is necessary for efficient LEC operation. These findings not only provide fundamental insights into the operational mechanism of LECs but also have direct implications for the broader organic mixed ionic and electronic conductor community.

The development of clean energy solutions requires an extension of electrical grids in size and power. Network evolution modeling is crucial for revealing critical insights into transmission grid growth dynamics. This study proposes an efficient analytical framework based on spatial network physics to enhance previous models and deliver better predictions. Utilizing the French transmission grid as a case study, our methodology demonstrates its aptitude to estimate energy invested and consumed, as well as topological and spatial characteristics. This new approach balances computational efficiency with predictive accuracy, making it particularly valuable for building datasets for research and contributing to well-informed infrastructure development forecast science.

Metal hydrides are important across diverse applications, such as hydrogen storage, batteries, gas sensors, nuclear reactions, and high-temperature superconductivity. Previous computational studies of metal hydrides under extreme pressures, e.g., $O({10}^2)\mathrm{GPa}$, usually treat them as stoichiometric compounds without considering interstitial lattice disorder. As pressures become more moderate in the $O({10}^0)\mathrm{GPa}$ and below range, hydrogen disorder at interstitial lattice sites becomes prominent, e.g., in the $\mathrm{N}$-doped $\mathrm{Lu}$ hydride that was recently claimed superconducting near 1 GPa. Further adding compositional complexity from alloying and/or multielement interstitial occupation makes elucidating pressure- and temperature-dependent observables intractable by first-principles calculations alone. We therefore propose a lattice graph neural-network surrogate modeling approach to predict configuration- and pressure-dependent equation-of-state properties. Their efficiency permits Monte Carlo simulations to calculate Gibbs energies and pressure-dependent phase diagrams, thereby revealing insights into the synthesis conditions required for achieving desired phase equilibria. We demonstrate this concept for the compositionally complex cubic ${\text{Lu(H,N,Va)}}_3$ system where three constituents (hydrogen, nitrogen and vacancy) have disordered multielement interstitial occupancies and insights into pressure-dependent phase equilibria are critically needed, e.g., $\mathrm{N}$-doping levels can significantly lower dehydrogenation temperatures and provide a new strategy to optimize hydrogen-storage alloys. This work can improve the thermodynamic understanding of the $\mathrm{Lu}$-$\mathrm{H}$-$\mathrm{N}$ system and help rational synthesis of $\mathrm{N}$-doped $\mathrm{Lu}$ hydrides, but more generally demonstrates an efficient approach to model pressure-dependent thermodynamics of multicomponent solid solutions.

We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud three-dimensional structure database of experimentally known inorganic stoichiometric compounds. We first compute the Allen-Dynes critical temperature ($T_c^{\mathrm{AD}}$) for $4533$ nonmagnetic metals using a direct and progressively finer sampling of the electron-phonon couplings. For the candidates with the largest $T_c^{\mathrm{AD}}$ value, we use automated Wannierizations and electron-phonon interpolations to obtain a high-quality data set for the most promising $250$ dynamically stable structures, for which we calculate spectral functions, superconducting band gaps, and isotropic Migdal-Eliashberg critical temperatures. For $140$ of these, we also provide anisotropic Migdal-Eliashberg superconducting gaps and critical temperatures. The approach is remarkably successful in finding known superconductors and we find $24$ unknown ones with a predicted anisotropic $T_c$ value above 10 K. Among them, we identify a possible double-gap superconductor ($p$-doped $\mathrm{Ba}\mathrm{B}$${}_2$), a nonmagnetic half-Heusler $\mathrm{Zr}\mathrm{Ru}\mathrm{Sb}$, and the perovskite $\mathrm{Ta}\mathrm{Ru}$${}_3$$\mathrm{C}$, all exhibiting significant $T_c$ values. Finally, we introduce a sensitivity analysis to estimate the robustness of the predictions.

The interplay between photochemical and electrochemical reactions fundamentally influences charge transfer processes at solid-liquid interfaces. Nevertheless, chemical processes at semiconductor surfaces triggered by light excitation under an applied potential remain poorly explored. This work deciphers the synergistic effect of potential and light excitation on boron-doped diamond electrodes in producing either surface redox reactions or emission of solvated electrons in water. The effect of diamond surface termination on electron affinity, band bending, and charge extraction is identified in a photoelectrochemical cell. While photocurrent is observed for excitation as low as 3.5 eV, we show that it is induced mostly by surface redox reactions, whereas solvated electrons are detected only for excitation above the band gap (5.47 eV). Solvated electrons are generated irrespective of band bending, which affects only the emission yield. Depending on the surface band bending, photoreduction of the hydroxylated surface groups and photooxidation of the C—$\mathrm{H}$ surface groups can be induced by direct photoexcitation in the range of 4.2–4.8 eV. The surface of the diamond can be electrochemically reduced when the Fermi level of the oxidized surface decreases below the ${\mathrm{H}}^{+}$/${\mathrm{H}}_2$ redox potential. On the other hand, the hydrogenated surface oxidizes spontaneously for potentials at which the Fermi level drops below the occupied $\text{C---H}$ surface states, depending on both the pH and the electron affinity of the surface. This work provides insights into (photo)redox processes on diamond materials, which may find applications in photoelectrochemical solar fuel generation or energy storage.

Lithium thiophosphate (LPS) is a promising solid electrolyte for next-generation lithium-ion batteries due to its superior energy storage, high ionic conductivity, and low-flammability properties. Despite its potential, the high reactivity of LPS with common contaminants such as atmospheric water, preparation solvents, and electrode materials poses significant challenges for commercialization. The lack of understanding regarding the structure, morphology, and chemical behavior of the surface of LPS slows down the search for solutions to these issues. Here, we utilize a machine-learning interatomic potential to achieve a fundamental atomistic understanding of the mechanical and chemical properties of the $\beta$-$\mathrm{Li}$${}_3$$\mathrm{PS}$${}_4$ surfaces. Employing molecular-dynamics simulations, we identify relevant complexions formed by surface reconstructions, determine their surface energies, and compute the Wulff shape of $\beta$-LPS. The most stable complexions exhibit properties distinctly different from the bulk, including amorphization, increased density, decreased conductivity, and large deformation of the structural building blocks. We demonstrate that these surfaces are not static but undergo significant dynamical activity at room temperature, which is clearly identified by an analysis featuring a time-averaged structural descriptor. Finally, we examine the changes of the electronic structure induced by the surface complexions as well as their effect on the reaction enthalpy with water, which provides us with details on changes in surface reactivity and active sites, underlining the importance of investigating surface complexions under realistic conditions.

Point defects are ubiquitous in materials and play a critical role in determining their electrical, optical, ion-transport, and other properties, making them key to the performance of materials in energy applications. First-principles defect calculations, particularly those based on density functional theory, are powerful tools for predicting defect energetics and associated electronic properties. However, the results are often communicated through defect formation energy diagrams and defect level diagrams, which can be challenging to interpret for researchers who do not specialize in defect physics. This Tutorial provides a practical accessible guide for understanding and interpreting these diagrams, with a focus on materials with a band gap—semiconductors and insulators. Aimed at experimentalists and computationalists new to defect analysis, the Tutorial explains how to extract key physical insights from defect diagrams, such as defect and carrier concentrations, dopability, midgap states, and charge localization. By demystifying the language and visuals of defect calculations, this guide seeks to bridge the gap between theory and experiments, enabling broader use of defect data in the rational design of functional materials for energy applications and beyond.

We investigate the layered magnetic semiconductor ${\mathrm{Eu}\mathrm{Te}}_4$, focusing on its intricate charge-density-wave (CDW) states near and above room temperature through single-crystal x-ray diffraction (XRD), magnetic, and thermoelectric measurements. The XRD measurement revealed that the CDW state inducing the polar lattice distortion persists even at 650 K, demonstrating its remarkable thermal stability. Notably, the Seebeck coefficient near room temperature reaches values exceeding 500 µV K${}^{-1}$. This large Seebeck coefficient, not fully captured by a simple band calculation, is comparable to those observed in heavy-electron semiconducting oxides, suggesting the importance of electron correlation and spin- and lattice instabilities. Furthermore, potentially reflecting the competition of two types of CDW state, the thermal conductivity near room temperature is as low as 0.02 W cm${}^{-1}$ K${}^{-1}$. As a result, the thermoelectric figure of merit zT reaches 0.22 at 460 K. These findings establish ${\mathrm{Eu}\mathrm{Te}}_4$ as a compelling platform to explore novel types of thermoelectric materials with multiple electronic instability.

Magnesium-ion batteries (MIBs) are promising next-generation energy storage devices. Vanadium oxides are promising cathode materials for MIBs due to their rich chemical valences and versatile structures. However, vanadium oxide cathodes face challenges such as low structural stability, slow ${\mathrm{Mg}}^{2+}$ diffusion kinetics, and low electrical conductivity. The work reported herein used hydrothermal synthesis to pre-intercalate ${\mathrm{H}}_2\mathrm{O}$ molecules and ${\mathrm{Mg}}^{2+}$ into ${\mathrm{V}}_2{\mathrm{O}}_5$ interlayers to stabilize the lamellar structure and modulate $\mathrm{V}$ 3d orbital splitting, leading to a ${\mathrm{Mg}}_{0.2}{\mathrm{V}}_2{\mathrm{O}}_5\cdot 1.5{\mathrm{H}}_2\mathrm{O}$ cathode (MVOH1). The $\mathrm{V}$ 3d orbital splitting facilitates electron hopping between ${\mathrm{V}}^{4+}$ and ${\mathrm{V}}^{5+}$ cations, leading to the enhanced conductivity of MVOH1; it also makes it easier for $\mathrm{V}$ atoms to acquire and lose electrons during discharging and charging, leading to high capacity and high rate capability of MVOH1. Consequently, the MVOH1 cathode exhibited a high specific capacity of 300 mA h ${\mathrm{g}}^{-1}$ at a current density of 0.05 A ${\mathrm{g}}^{-1}$ and retained a specific capacity of 85 mA h ${\mathrm{g}}^{-1}$ at 10 A ${\mathrm{g}}^{-1}$; the capacity and rate capability surpass those of previously reported ${\mathrm{V}}_2{\mathrm{O}}_5$ cathode materials. The aqueous MIB based on MVOH1 demonstrated an ultralong lifespan of 11 000 cycles, which is significantly longer than that of previously reported aqueous MIBs. This study highlights the synergistic effect of regulating interlayer spacing and $\mathrm{V}$ 3d orbital splitting on electrochemical performance, which is crucial for developing more efficient and more durable energy storage systems.

Simultaneously achieving interfacial stability and high room-temperature ionic conductivity ($\ge {10}^{-3}$ S/cm) in solid-state electrolytes is one of the most significant challenges in the development of all-solid-state batteries. In this study, by combining first-principles simulations and large-scale machine-learning molecular dynamics modeling, we demonstrate ${\mathrm{K}}_5{\mathrm{YSi}}_4{\mathrm{O}}_{12}$ is such an outstanding electrolyte for $\mathrm{K}$-ion batteries that showcases both intrinsic interfacial stability with metal anode and fast ion diffusivity. Conversely, ${\mathrm{Li}}_5{\mathrm{YSi}}_4{\mathrm{O}}_{12}$ only exhibits mediocre performance for $\mathrm{Li}$-ion batteries. Our results show ${\mathrm{Li}}_5{\mathrm{YSi}}_4{\mathrm{O}}_{12}$ is thermodynamically unstable and will be reduced by the metal anode, whereas thermodynamically stable ${\mathrm{K}}_5{\mathrm{YSi}}_4{\mathrm{O}}_{12}$ presents a wide electrochemical stability window of 0.00–3.27 V versus $\mathrm{K}/{\mathrm{K}}^{+}$, suggesting it is stable in contact with potassium metal. The predicted ionic conductivity is 0.43 and 3.63 mS/cm for ${\mathrm{Li}}_5{\mathrm{YSi}}_4{\mathrm{O}}_{12}$ and ${\mathrm{K}}_5{\mathrm{YSi}}_4{\mathrm{O}}_{12}$, respectively. Moreover, a significant size effect has been observed in ${\mathrm{K}}_5{\mathrm{YSi}}_4{\mathrm{O}}_{12}$, where the accurate ionic conductivity can only be obtained through large-scale simulations. Further analysis reveals low energy barrier and long hopping distance are responsible for the higher ionic conductivity in ${\mathrm{K}}_5{\mathrm{YSi}}_4{\mathrm{O}}_{12}$. This work identifies an excellent electrolyte candidate for $\mathrm{K}$-ion batteries and underscores the importance of scale in the modeling of ionic conductivity.