The fast cycling capability of Li-ion batteries is a key performance indicator for a range of applications including electric vehicles (EVs), in which systems must deliver/accept high currents while avoiding excessive degradation. We evaluate four leading EV battery chemistries in this context, encompassing cathodes focused on design-to-performance, ${\text{LiNi}}_{0.8}{\text{Mn}}_{0.1}{\text{Co}}_{0.1}{\mathrm{O}}_2$ (NMC811), and design-to-cost, ${\text{LiMn}}_{0.6}{\text{Fe}}_{0.4}{\text{PO}}_4$ (LMFP), alongside current state-of-the-art (graphite and $\text{graphite}@{\text{SiO}}_{\mathrm{x}}$ composite) and next-generation (high ${\text{SiO}}_{\mathrm{x}}$) anodes. These chemistries are adopted as model systems to examine three common test protocols for evaluating fast cycling, specifically tuned to fast charge, fast discharge, or both. The capacity, degradation induced, and operational state-of-charge range were highly dependent on selected testing protocols. Significant loss of lithium inventory (LLI) was observed under fast-charging conditions, associated with lithium plating, observed via monitoring of anode potential in three-electrode cells, differential voltage analysis, and visual inspection of electrodes postmortem. LMFP cells induced a greater level of Li plating than NMC, reaching negative anode potentials in the early stages of fast charging. While the ${\text{SiO}}_{\mathrm{x}}$ anode performed relatively well at high rates, it suffered a greater degree of degradation, including both LLI and impedance increase associated with excess SEI growth. This study provides a basis for battery material design and charging protocols tuned for fast cycling.

Osmotic energy systems employ charged nanoporous membranes and nanochannels to convert salinity gradients into electrical power, offering a promising route to harvest one of Earth’s most abundant yet underexploited renewable resources—the oceans—and providing reliable, carbon-free electricity. However, to date, no known self-consistent theoretical model can accurately predict the electrical response of realistic charge-selective systems. Thus, empirical and phenomenological models are used to increase the performance while attributing the increase to various mechanisms, which are mostly incorrect and incomplete. This leads to a costly empirical trial-and-error optimization process, which typically yields only incremental improvements. Here, we provide a self-consistent theoretical model for the current-voltage ($I-V$) response, verified by nonapproximated numerical simulations, that establishes a detailed framework for osmotic energy conversion in realistic systems comprising a charge-selective membrane and the often-neglected larger bulk reservoirs. Our model delineates the effects of the surface charges, the bulk concentration ratio, and the often-neglected effects of the bulk reservoirs. The model provides analytical expressions for all the major transport characteristics at zero current ($I=0$), including the Ohmic resistance, $R_{\text{Ohmic}}$, the voltage at zero current, $V_{I=0}$, and the transport number, $\tau$, which is a metric for the selectivity of the system. The $I=0$ insights are carried over to the results at the energy harvesting limit of zero voltage ($V=0$), allowing a straightforward analysis. Two of our key results are unexpected. First, the concentration at which the harvestable electrical power is maximal is not the concentration at which the harvestable electrical current is maximal. Second, overlooking the effects of the bulk reservoirs leads to overestimations of the harvestable power by several orders of magnitude. Our proposed framework lays the theoretical foundation for a new generation of sustainable nanofluidic energy systems relevant to addressing global energy and water challenges.

Quantifying the energetic cost of implementing quantum operations is essential for assessing the efficiency and scalability of quantum sensing and information-processing technologies. Here, we introduce a framework for analyzing the interplay between complexity and energy cost of quantum processes. In particular, we apply our framework to a sequential quantum phase estimation protocol, where a phase of physical significance is encoded in a quantum channel. The channel is applied to a probe state repeatedly until the probe is measured and the outcome leads to an estimate on the phase. We establish a trade-off relation between the total energy cost of the protocol and the number of times the channel is applied (complexity), while reaching a desired estimation precision. A sweet spot is located where the two quantities are co-optimized. The principles of our analysis can be adapted to benchmark the energetic requirements in other quantum protocols and devices.

Layered perovskite lithium yttrium titanate (${\text{LiYTiO}}_4$) has recently emerged as a promising low-potential, ultrahigh-rate anode material for lithium-ion batteries. However, its lattice dynamics and thermal transport behavior remain largely unexplored, limiting a complete assessment of its practical applicability. Here, we combine experimental thermal conductivity measurements with a systematic theoretical study based on a machine-learning neural evolution potential (NEP). Our computational framework integrates the temperature-dependent effective potential method with the Wigner thermal transport (WTT) formalism, explicitly accounting for both diagonal and off-diagonal components of the heat-flux operator. Phonon calculations at 0 K reveal dynamical instabilities arising from rotations of the ${\mathrm{TiO}}_6$ octahedra, which are stabilized at finite temperatures through anharmonic phonon renormalization. By incorporating particlelike phonon propagation and wavelike phonon tunneling within the WTT framework, we predict an orientation-averaged room-temperature lattice thermal conductivity (${\kappa }_{\mathrm{L}}$) of $3.6{\text{Wm}}^{-1}{\mathrm{K}}^{-1}$, which is qualitatively consistent with the experimentally measured value of $3.2\pm 0.08{\text{Wm}}^{-1}{\mathrm{K}}^{-1}$ for polycrystalline samples. To further elucidate the role of ionic motion at elevated temperatures, we compute ${\kappa }_{\mathrm{L}}$ using the Green-Kubo approach within equilibrium molecular dynamics simulations based on the trained NEP, yielding improved agreement with experiment relative to the WTT prediction. Our results identify intrinsically low thermal conductivity as a key limitation for the practical application of ${\text{LiYTiO}}_4$ and provide broader insight into anharmonic lattice dynamics and thermal transport in complex battery materials.

Recent simulations and observations suggest that clouds interact with offshore wind farms, yet the physical mechanisms governing this interaction remain unclear. Here, we investigate these processes using large-eddy simulations of stratocumulus-topped boundary layers, systematically removing key forcings to isolate their physical effects. We consider three progressively simplified scenarios: (i) a fully developed marine stratocumulus boundary layer, (ii) a case without radiative forcing, which weakens both cloud-top longwave cooling and entrainment, and (iii) a clear-sky case in which both radiative forcing and liquid water are removed. The presence of cloud-top radiative cooling increases entrainment, leading to a less stable and deeper boundary layer that promotes vertical transport of momentum and energy toward the turbines. This leads to a faster wake recovery, with direct implications for the performance of neighboring wind farms. Overall, our results demonstrate that radiatively driven cloud-top entrainment is a key mechanism linking cloud dynamics to the performance of large wind farms and underscore the need for wind-energy models that explicitly represent interactions among wind farms, the atmosphere, and cloud processes.

Nonlinear thermoelectric effects offer the potential to enable new energy technologies, such as voltage-controlled thermal switching and thermoelectric rectification. In this work, we examine how the quantum geometry of the electron bands gives rise to nonlinear thermoelectric responses in materials with suitable symmetries. We derive a series of nonlinear thermoelectric effects governed by the Berry curvature dipole and the quantum metric dipole, respectively. Among them, we identify a particularly interesting quantized thermoelectric response that directly measures the total chirality of Weyl points below the Fermi level. For general nonlinear responses, we derive the nonlinear analogs of the Wiedemann-Franz law and Mott’s formula. These provide a means to estimate the magnitude of nonlinear thermoelectric responses based on existing nonlinear Hall measurements. Our estimates suggest that these effects should be observable in several candidate materials, with In-doped ${\text{Pb}}_{1-x}{\text{Sn}}_x\text{Te}$ standing out as the most promising. Our work offers new insights into the experimental study of quantum geometry through nonlinear thermoelectric measurements.

A minimal model of ionic aggregation in concentrated ${\text{ZnCl}}_2$ is developed, guided by molecular dynamics simulations with a machine-learned potential. It explicitly incorporates solvent-site depletion, correlated chloride binding, and allows for loops within Zn–Cl clusters. Dehydration drives two coordination-controlled structural changes: a crossover at $Z=2$ from predominantly isolated Zn-containing species to Cl-bridged clusters and the onset of gelation near $Z\approx 3$. The model reproduces the concentration-dependent trends observed in the molecular dynamics simulations with two fitted parameters, while the cluster-size distribution at the highest concentration is consistent with three-dimensional percolation scaling over the accessible range of cluster sizes.

The introduction of Se in CdTe solar-cell absorbers has led to multiple beneficial effects in the devices, including an increased short-circuit current and an extended charge-carrier lifetime. A critical manufacturing step of ${\mathrm{CdSe}}_x{\mathrm{Te}}_{1-x}$ solar cells is ${\mathrm{CdCl}}_2$ annealing at high temperatures, during which Se diffuses through the absorber layer. Understanding the local Se distribution in the absorber and its impact on optical properties of the material is key for the further development of this thin-film solar cell technology. In a multimodal x-ray scanning microscopy approach, we investigated the impact of the annealing temperature of ${\mathrm{CdSe}}_x{\mathrm{Te}}_{1-x}$ solar cells on the Se distribution and on optical characteristics of the material. For this purpose, we exploited an upgraded x-ray excited optical luminescence (XEOL) detection unit that enables simultaneous spectrally and temporally resolved XEOL mapping at synchrotron facilities that are ideal for studying thin-film solar-cell materials with hard x-rays featuring high penetration depth and lateral resolution.

Electrode morphology critically determines the stability and efficiency of lithium metal anodes, yet no predictive framework has explained how measurable parameters control deposition. Here we introduce the first theoretical model of electrochemical Ostwald ripening, capturing the competition between electroplating and surface-energy-driven redistribution and identifying it as the governing process behind morphology evolution in the nondendritic regime. The framework explicitly incorporates solid electrolyte interphase (SEI) resistance, electrolyte conductivity, electrode wettability, and current density, revealing the transition from a 2D SEI-limited to a 3D electrolyte-limited growth. The model yields analytical expressions for nucleus size, density, and distribution that quantitatively reproduce independent experimental results and establishes a direct link between plating conditions, morphology, and Coulombic efficiency. By providing experimentally accessible relationships between key parameters and deposition outcomes, the framework enables a predictive understanding of lithium plating and provides a broadly applicable basis for controlling electrodeposition morphology across diverse electrochemical systems.

Understanding electrochemical phenomena at complex liquid-solid interfaces requires linking real-time structural dynamics with atomic-scale interfacial chemistry. Here, we integrate in-situ synchrotron X-ray fluorescence and diffraction with high-resolution cryogenic electron and ion multimodal microscopy to provide a mechanistic understanding of Pt-based alloying anodes across length scales. We directly observe the initial lithiation-driven formation of ${\mathrm{Li}}_2\mathrm{Pt}$ and its evolution to a stable LiPt intermetallic phase during extended cycling via a solid-solution type reaction mechanism. Simultaneously, the solid-electrolyte interphase transitions from an unstable carbonate-rich to a stable LiF-dominated composition, confirmed by cryogenic scanning transmission electron microscopy-electron energy loss spectroscopy. Crucially, cryogenic atom probe tomography reveals spatially distinct compositional regimes within the alloy anode: a lithium-flux-limited, heterogeneous interfacial zone and a diffusion-controlled, homogeneous LiPt alloy bulk. This nanoscale compositional gradient rationalizes the emergent solid-solution reaction mechanism and highlights how kinetic limitations and interface dynamics govern alloy formation and electrochemical stability. Our findings establish a correlative experimental framework that directly links in-situ structural dynamics with preserved near-atomic resolution interfacial chemistry, advancing the rational design of durable alloy electrodes for next-generation energy storage.

Alkaline water electrolyzers (AWEs) offer a carbon dioxide-emission-free route to convert renewable electricity into hydrogen. Improving their efficiency requires a deep understanding of transport and kinetics within an electrolytic cell. By combining molecular simulations of ion diffusivities with detailed multiphysics simulations, we report here a surprising finding that neglecting the temperature dependence of ion transport in an AWE could lead to erroneous predictions of current density reducing with increasing temperature. This is because ion mobilities reduce with temperature, leading to severe transport limitations if diffusivities are considered to be independent of temperature. We model ion diffusion, migration, and convection via the Nernst-Planck formulation, fluid dynamics via the continuum Euler-Euler approach, along with electrode kinetics at the Butler-Volmer level of theory. We carry out molecular dynamics simulations to determine the temperature dependence of ${\text{OH}}^{-}$ and ${\mathrm{K}}^{+}$ diffusivities and show that considering temperature-dependent diffusivities leads to the current density increasing with temperature. We also demonstrate that gas bubble formation and ${\text{OH}}^{-}$ concentration gradients lead to reduced electrolyte conductivity near the electrodes. The higher levels of cathodic gas production, as compared to those at the anode, lead to asymmetries in the concentration profiles. We provide physical understanding regarding these observations, including that at high cell potentials, transport limitations play a crucial role in modulating AWE performance. Overall, our findings provide critical insights into the interplay between ion transport, temperature, and electrochemical performance, which are crucial for accurate multiscale simulations and modeling-based optimization of such devices.

We estimate the run-time and energy consumption of simulating nonequilibrium dynamics on neutral-atom quantum computers in analog mode, directly comparing their performance to classical methods, namely matrix product states and neural quantum states. By collecting both experimental data from a quantum processing unit (QPU) in analog mode and numerical benchmarks, we enable accurate predictions of run-time and energy consumption for large-scale simulations on both QPUs and classical systems through fitting of theoretical scaling laws. Our analysis shows that neutral-atom devices are already operating in a competitive regime, achieving comparable or superior performance to classical approaches while consuming significantly less energy. These results demonstrate the potential of analog neutral-atom quantum computing for energy-efficient simulation and highlight a viable path toward sustainable computational strategies.

Photovoltaics play a key role in the renewable energy transition, where perovskite solar cells stand out as one of the most promising with high-power conversion efficiencies, desirable characteristics, and great versatility. The efficiency of these cells is mainly limited by nonradiative recombination, whether at one of the interfaces between the perovskite layer and transport layers or within the bulk of the perovskite itself. However, it is difficult to probe which of these dominates the losses during operation, which complicates further improvements. Here we introduce and demonstrate a simple, inexpensive, and easy-to-integrate method for identification of the limiting aspect of perovskite solar cells under operating conditions in terms of recombination losses. We illuminate a device with red, blue, or white light, each absorbed differently depending on the position in the device. We show that, in perovskite solar cells, the diffusion length in the bulk is of secondary importance and rather the interfaces between layers dominate the charge carrier distributions, and thus light of different wavelengths will result in changing performance. Using the fill factor from the device characteristics for each case, we are able to identify whether the front or back interface of the cell limits the performance with nearly 97% accuracy. Finally, we apply this principle experimentally using a co-evaporated perovskite solar cell, where we show a decrease of nearly 5% in fill factor depending on the wavelength of light used.

We report on a rapid, nondestructive scanning laser curvature method for directly characterizing an important and unacknowledged factor that can contribute to the stability of encapsulated single-junction and tandem perovskite solar modules, i.e., the curvature from lamination. The ability to directly identify mechanical stresses and their evolution in fully packaged modules provides critical information regarding possible risks for accelerated degradation and failure. We define a metric for the curvature relating the magnitude and variance in front-glass $z$-displacement to the “flatness” of the module. We also study the effects of package design and lamination parameters—specifically, encapsulant volume, module layout, front- and back-glass thickness, and ribbon placement with the goal of reducing the curvature and the resulting mechanical stress imposed after lamination. Critically, we show that flatter modules show significantly enhanced durability when compared to curved modules, increasing ${\mathrm{T}}_{80}$ by about a factor of 2 from $\sim 900$ to 1800 h under ISOS-L2 conditions at $65°\mathrm{C}$. A direct link between mechanical stress imparted from lamination and chemical degradation mechanisms in modules provides an important design guideline to the community toward robust and reliable perovskite module manufacturing.

Bismuth vanadate is a promising photoanode material for photoelectrocatalytic water splitting. Localized charges play a crucial role in the water-splitting mechanism by introducing charge-transition levels within the band gap and thereby modifying the band alignment. Additionally, the introduction of oxygen vacancies, which can interact with electron polarons, has been reported to improve the photoelectrocatalytic efficiency. However, charge-transition levels are typically evaluated at 0 K, whereas operating conditions involve finite temperatures and, in thin films, epitaxial strain. Capturing these effects requires free energies and extensive sampling that are generally prohibitive for ab initio methods. In this work, we train a machine-learned interatomic potential for ${\text{BiVO}}_4$, including electron polarons and oxygen vacancies in various charge states, to resolve how free energies, transition levels, and charge trapping evolve under operando temperature and strain conditions. For oxygen vacancies, the use of a machine-learned potential enables efficient sampling of charge-localization sites in the vicinity of the vacancy, revealing favored configurations. The energies of the localized polaron and oxygen-vacancy states remain essentially unchanged with temperature, while their transition levels relative to the band edges vary due to temperature-dependent band-edge shifts. In contrast, strain more strongly modifies both the localized states and the band edges. These results provide new insights into the stability of localized charges and their role in photoelectrocatalytic water splitting at finite temperature.

Understanding the origin and mechanisms of defect-induced voltage losses and recombination in metal-halide perovskites aids in the development of new strategies to achieve higher solar cell efficiencies. Photoluminescence (PL) spectroscopy provides insight into charge recombination at defects in the perovskite and at interfaces with passivation and electron transport layers. Measuring the time-resolved PL as a function of temperature for a triple-cation mixed-halide perovskite (CsFAMA) reveals that the PL decay is dominated by shallow defects and that lowering the temperature changes the trap-energy landscape, making the shallow defects shallower until they vanish into the conduction or valence bands at 90 K. Depositing fullerene $({\mathrm{C}}_{60})$ on top of the CsFAMA perovskite induces significant nonradiative recombination losses at room temperature, which can largely be reduced by applying interfacial passivation. Interestingly, cooling to 90 K makes the charge-recombination dynamics almost identical for CsFAMA and $\text{CsFAMA}/{\mathrm{C}}_{60}$ films, irrespective of interfacial passivation treatments. At such low temperatures, nonradiative recombination losses become less dominant because interfacial recombination velocities are reduced. The results provide new insight into shallow defects and recombination losses in CsFAMA perovskites and help to better understand the effect of surface treatments on shallow-defect properties.

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.