The design of materials for electrochemical energy conversion is complicated by a vast search space of candidate materials and multifaceted property requirements: multicarrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Although self-driving laboratories are rapidly rising to address such material optimization problems, the required infrastructure for integrated, large-scale robotic facilities can be cost-prohibitive. Here we develop and evaluate a closed-loop measurement system for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers, building on top of an existing benchtop instrument and integrating techniques for rapid impedance measurement and automated analysis. This system exemplifies a “minimum viable” self-driving implementation that can deliver substantial benefits with relatively simple infrastructure. Combinatorial thin-film microelectrode libraries are characterized with a recently developed joint time-domain and frequency-domain impedance measurement technique, which provides an order-of-magnitude acceleration relative to conventional impedance spectroscopy. The distribution of relaxation times is extracted from impedance data and analyzed without human intervention. These results feed an active learning and Bayesian optimization process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces the screening time by tenfold with optimized experimental sequences. We apply this system to $\mathrm{Ba}(\mathrm{Co},\mathrm{Fe},\mathrm{Zr},\mathrm{Y}){\mathrm{O}}_{3-\delta }$ combinatorial libraries and evaluate its effectiveness for learning material property trends and optimizing expensive-to-evaluate properties such as activation energy. This offers insights into key methodological aspects of practical autonomous experimentation, including surrogate model validation, cost-aware acquisition functions, and high-throughput data interpretation. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight real-world experimental challenges of thin-film degradation and numerical instability in surrogate models.

The nuclear spin-isomer (ortho-para) composition of molecular hydrogen strongly influences the safety and efficiency of cryogenic hydrogen handling, as the energy of ortho-para conversion exceeds the enthalpy of vaporization of liquid hydrogen, generating boil-off losses. Accurate in situ measurement of spin-isomer composition at cryogenic conditions is therefore essential for liquefaction process design, spin-isomer catalyst characterization, and custody transfer. Here we demonstrate a microwave cavity resonator as a novel sensor for real-time quantification of hydrogen ortho-para composition. The method exploits contrast in the dielectric permittivity of the nuclear-spin isomers, enabling precise tracking of natural ortho-para conversion dynamics in solid (10.0 K), liquid (16.6–19.9 K), and supercritical (40.4 K) hydrogen. Measurement uncertainties averaged 0.007 mol/mol across all phases. During conversion, the resonant frequencies followed a second-order kinetic model consistent with theoretical predictions and prior studies, with final equilibrium compositions independently validated by Raman spectroscopy. The determined conversion rate constants agree with the empirical correlation of a previous study, while also revealing the potential errors introduced from measuring natural conversion kinetics over a limited concentration range. Furthermore, our results identify significant discrepancies between experimental dielectric data and current predictive models, underscoring the need to refine hydrogen property models at cryogenic conditions. This work establishes microwave cavity resonators as a powerful tool for ortho-para hydrogen sensing at cryogenic conditions, with applications spanning industrial liquid hydrogen process monitoring, hydrogen isomer conversion catalyst development, and high-energy physics.

The electric double layer (EDL) that forms at the interface between metals and ionic solutions is at the heart of various energy technologies. Recent experimental data have challenged our traditional understanding of the EDL charging behavior, which is based on mean-field Gouy-Chapman-Stern-type (GCS) models. In this article, we present a classical theory for the EDL, derived from first-principles statistical mechanics, that accounts for electron-ion correlation effects using the method of image charges and systematically extends beyond the mean-field level. Such electron-ion correlations introduce an additional interaction between the metal surface and electrolyte ions, significantly altering the EDL structure. Our theory, valid in the limit of dilute electrolyte solutions and weakly charged metal surfaces, achieves quantitative agreement with experimental capacitance data across a wide range of electrode materials and electrolyte solvents, and thus resolves long-standing questions on the origin of discrepancies to GCS predictions. Thereby, the framework conceptually unifies the processes of double-layer charging and ion adsorption, which are typically considered as distinct phenomena, but are shown to be manifestations of the same fundamental electrostatic principles.

High-entropy materials offer a promising avenue for thermoelectric materials discovery, design, and optimization. However, the large chemical spaces that need to be explored hamper their development. In this work, a large family of high-entropy skutterudites is explored as promising thermoelectric materials. Their potential synthesizability is screened and rationalized using the disordered enthalpy-entropy descriptor through high-throughput density functional theory calculations. In the case of high-entropy skutterudites, the thermodynamic density of states and the entropy gain parameter appear to be key factors for their stabilization. Electronic band structure analyses not only show a reduction in the band gap, which enhances carrier concentration and electrical conductivity, but also a band convergence phenomenon for some specific compositions, which is related to the “cocktail effect.” Analyzing atom-projected band structures shows how band convergence is due to the simultaneous presence of $\mathrm{Fe}$, $\mathrm{Ni}$, and $\mathrm{Co}$ in the compound. The presence of $\mathrm{Rh}$ or $\mathrm{Ir}$, while not contributing to this band-convergence effect, can be directly linked to an increase in the system’s entropy, which enhances the thermodynamic stability of these materials. Transport properties are computed for the most promising compositions, and their dynamical, mechanical, and thermal stability are addressed. Our results demonstrate that these types of compounds open new avenues, not only to enhance thermoelectric efficiency but also to reduce costs by utilizing more abundant elements and also improving their durability.

Fully electrifying the building sector requires not only the widespread adoption of photovoltaic (PV) self-consumption and heat pumps, but also the integration of cost-effective energy-storage solutions. Hybridizing lithium-ion ($\mathrm{Li}$-ion) batteries with power to heat to power storage (PHPS) systems—thermal batteries capable of thermal-to-electric energy conversion—offer a promising and economically viable solution. PHPS systems dispatch combined heat and power by utilizing the low-temperature waste heat generated during the thermal-to-electric energy-conversion process. This study investigates the technoeconomic impacts of waste heat use in PHPS systems integrated with $\mathrm{Li}$-ion batteries and heat pumps to support the decarbonization of the building sector. Two distinct strategies are evaluated: (1) direct use of waste heat to meet heating demands; and (2) the use of waste heat to enhance the heat pump’s coefficient of performance. Results show that supplying the waste heat at the demand setpoint temperature is the best solution to integrate PHPS cost effectively, although enhancing the heat pump’s COP with waste heat also yields notable economic gains. Additionally, leveraging waste heat significantly lowers the minimum thermal-to-electric conversion efficiency required for PHPS systems to achieve economic viability. Optimal PHPS designs enable large-scale energy storage and charging capacities, thereby enhancing PV self-consumption rates and reducing the levelized cost of energy. The analysis also reveals that hybridizing PHPS with $\mathrm{Li}$-ion batteries may rise as the optimal solution for moderately priced PHPS systems, with the reduction in levelized cost being more pronounced in solar-dominated regions.

${\mathrm{Nb}}_3\mathrm{Sn}$ is the workhorse superconductor for high-field applications; however, several fundamental aspects remain open 70 years after its discovery. Using anharmonic ab initio Migdal-Eliashberg theory, we resolve three key questions: (1) the cubic-to-tetragonal martensitic transition is weakly first order; (2) the superconducting gap is strongly anisotropic but continuous over the Fermi surface; and (3) the drop of $H_{c2}$ across the transition arises from both reduced electron-phonon coupling and a redistribution of Fermi velocities. The orbital origin of the gap anisotropy suggests targeted doping strategies: $\mathrm{Sn}$-site substitution can increase both $T_c$ and $H_{c2}$, while $\mathrm{Nb}$-site substitution raises $H_{c2}$ at the cost of lowering $T_c$.

Carbon dioxide (${\mathrm{CO}}_2$) levels in our atmosphere continue to reach new historic highs every day. In 2022, industry made up 30% of United States greenhouse gas emissions, including electricity end-use indirect emissions. Simultaneously, personal consumption expenditures increased by 9.2% in 2022 and continue to rise, indicating that there is an increasing need for decarbonization in production processes, with a tandem effort to combat overconsumption. One class of technologies that has garnered interest from researchers, policymakers, and communities is carbon capture. “Point source carbon capture” specifically refers to capturing ${\mathrm{CO}}_2$ at its point of emission before it is released to the environment. However, as further scrutiny is put on carbon capture technology, it is clear that further research is needed and more options are necessary to ensure that our goal of reducing greenhouse gas emissions is not done without care. Carbon capture and sequestration or utilization have been touted as possible solutions for our growing demands, but present a range of health, safety, and environmental challenges, including siting concerns and possible leakages of pipelines or storage facilities. A relatively newer technology, reactive carbon capture (RCC), provides a potential pathway that minimizes some of those concerns. RCC converts ${\mathrm{CO}}_2$ to valuable fuels and chemicals immediately after it is captured, eliminating the need for energy-intensive desorption from capture agents, as well as ${\mathrm{CO}}_2$ storage and transport through pipelines. Thus, the entire process is contained at existing industrial plant sites, minimizing the span of environment and frontline communities that may be impacted by potential leaks and pollution. However, there are also a number of remaining challenges in RCC technology that must be addressed, alongside the need to decrease consumption, in order to be in accordance with environmental justice principles and make way for safe and effective technology adoption.

Thermophotonic (TPX) devices offer a promising pathway to overcome the power density and efficiency trade-offs of thermophotovoltaics (TPVs) by using biased light-emitting diodes (LEDs) as active infrared photon emitters. However, practical implementation of TPXs has been limited by stringent demands requiring the LED to operate at high temperatures, as well as the need for near-perfect external quantum efficiency (EQE) to enable self-sustaining (i.e., no external power supply) operation. Here, we introduce a zero-vacuum-gap thermophotonic (zTPX) architecture that integrates a high-refractive-index solid-state spacer between the LED and the photovoltaic cells, which forms an optical cavity that minimizes index mismatch and enables high photon concentration. This design enhances both power density and efficiency, over 40-fold power improvement, and up to doubled efficiency enhancement compared to conventional far-field TPX, allowing the operation of TPXs at relatively lower temperatures, at which high-performance LEDs are available. Furthermore, we show that the zTPX structure achieves a record-breaking EQE of approximately 98%, which enables a self-sustaining TPX circuit with a sizable power output. These combined performance gains position zTPX as a highly practical thermophotonic solution for thermal energy conversion and utilization at low to moderate temperatures (<1000 °C), including industrial waste-heat recovery, distributed power generation, and portable heat-to-electricity converters.

Accurate machine-learning (ML) prediction of material energies typically relies on large models with thousands to hundreds of thousands of parameters and scales poorly with the system size. Here, we present a compact hybrid classical-quantum ML framework that predicts the energies of complex materials using fewer than ten qubits, regardless of system size. By compressing local atomic descriptors into a fixed-size feature spectrum, we enable amplitude encoding of chemically diverse systems into shallow quantum circuits. We implement quantum convolutional neural networks (QCNNs) and quantum deep neural networks (QDNNs) with fewer than $100$ trainable parameters and achieve prediction errors below 0.05 eV/atom on chemically complex alloys ($\mathrm{Co}$${}_{25}$$\mathrm{Ni}$${}_{25}$($\mathrm{Hf}$$\mathrm{Ti}$$\mathrm{Zr}$)${}_{50}$) and lithium-intercalated metal-organic frameworks. Our results demonstrate the feasibility of scalable quantum assisted materials modeling and introduce a resource-efficient route toward quantum machine learning for atomistic simulations.

In the quest to accelerate scientific discovery, the materials science field is rapidly moving toward the implementation of robotics and artificial intelligence driven workflows. Our recent summer school “Future Labs: Robotic Synthesis Coupled with Machine Learning for Energy Materials” provided learning opportunities for students, researchers, and educators in the materials science community. We describe this experience and provide our perspective on which new directions could be pursued to enable the future workforce to acquire cross-disciplinary skills.

The power-conversion efficiency of organic solar cells has recently surpassed 20%, yet a unified framework that captures the underlying photophysical mechanisms remains elusive. These devices operate via a complex interplay between multiple excited states—excitons, charge transfer (CT), and charge-separated states—arising at the donor-acceptor heterojunction. Here, we present a comprehensive 5-state kinetic model that, for the first time, integrates both singlet and triplet CT states and explicitly incorporates the formation and resplitting of local triplet excitons, fully parametrized by the interfacial energy offset. This model not only reproduces essential photovoltaic observables—such as charge generation efficiency, photoluminescence, electroluminescence, and the Langevin reduction factor—but also reveals that triplet-mediated recombination governs device performance across a wide range of energy offsets. Strikingly, we find that in systems with moderate offset, triplet decay emerges as the dominant recombination pathway, reconciling long-standing experimental findings, including those in benchmark systems such as PM6:Y6. The model offers a mechanistic explanation for the empirically observed link between energy offset and non-Langevin recombination, and accurately predicts the device efficiency across different material systems. Notably, it identifies a singlet-CT offset of ∼150 meV as optimal for efficient charge separation while suppressing loss pathways. These results advance the fundamental understanding of exciton and charge dynamics in organic photovoltaics, and provide predictive guidance for overcoming the apparent efficiency ceiling of ∼20% for binary donor-acceptor blends, paving the way for next-generation high-performance devices.

Electron-phonon coupling (EPC) is key to understanding charge transport, band renormalization, and superconductivity in energy materials, including correlated transition-metal oxides, ferroelectric perovskites, optoelectronic semiconductors, and phonon-mediated superconductors. Although first-principles density functional theory (DFT)-based EPC calculations are used widely, their predictive power is limited by the accuracy, transferability, and efficiency of the underlying exchange-correlation functionals. These limitations become exacerbated in complex $d$- and $f$-electron materials, where beyond-DFT approaches and additional corrections, such as the Hubbard $U$, are commonly invoked. Here, using the examples of $\mathrm{Co}\mathrm{O}$ and $\mathrm{Ni}\mathrm{O}$, we show how the $r^2$SCAN density functional correctly captures strong EPC effects in transition-metal oxides without requiring the introduction of Hubbard $U$ parameters. We also find that $r^2$SCAN successfully describes the subtle interplay between ionic and covalent bonding, and strong EPC effects in the low-temperature rhombohedral phase of the prototypical ferroelectric $\mathrm{Ba}\mathrm{Ti}\mathrm{O}$${}_3$, without requiring Hubbard $U$ and intersite $V$ corrections. We further demonstrate the ability of $r^2$SCAN to accurately model the EPC of the main-group semiconductor $\mathrm{Ga}\mathrm{As}$ and the phonon-mediated superconducting properties of $\mathrm{Mg}\mathrm{B}$${}_2$, with reliable electronic bands and phonons. Our study establishes $r^2$SCAN-based EPC as a transferable, parameter-free, and computationally efficient framework for predictive material-specific modeling of EPC in energy materials, and opens a practical route toward high-throughput screening of superconductors, thermoelectrics, optoelectronic semiconductors, and oxide electrodes and catalysts.

The demand for high energy efficiency drives intense interest in thermal-management technology. Phonons are a major contributor to heat transfer in solids and controlling them through external stimuli is a key challenge for thermal management due to their weak interactions with applied fields. We report significant changes in phonon transport with the application of an electric field over a broad temperature range in a relaxor-based ferroelectric using neutron-scattering and -transport measurements. Phonon lifetimes increase along the applied poling field and this results in a tripling of the thermal conductivity in that direction. We also observe a suppression of nanoscale antiferroelectric fluctuations along the poling direction and argue that this increases the phonon lifetimes. Our results highlight a promising yet underexplored route to realistic solid-state heat switching from electric-field-modified nanostructures altering the phonon lifetimes in disordered functional materials.

Achieving sustained high plasma confinement both in the core and at the edge, without crashes due to edge instabilities, is highly desirable for efficient fusion energy production in a tokamak device. However, control of edge instabilities usually results in core plasma confinement degradation. Here we demonstrate a new improved core confinement plasma regime in EAST, characterized by a sustainable internal transport barrier (ITB) and effective control of edge instabilities, both achieved by applying small resonant magnetic field perturbations (RMPs) at just 0.1% of the equilibrium field amplitude. The key is to localize the perturbations at the edge, which can be effectively realized by use of RMPs with high toroidal mode numbers. Nonlinear interaction between the perturbed magnetic fields and plasma, especially tungsten impurities, on different scales plays a key role in lowering the threshold heating power by a factor of 2 for triggering the formation of an ITB to improve core confinement. More importantly, the improved core confinement can be actively controlled by tuning the perturbed field profile. This provides an attractive method and a physical basis for an integrated solution of keeping high core confinement during edge stability control for a future fusion reactor.

Although 43% of Africa’s population (amounting to about 600 million people) have no access to electricity, Africa still has 40% of the global potential for solar power. In 2022, Africa produced 12 641 MW of electricity from solar energy, amounting to 1.2% of the global production of solar electricity. Of this, 11 516 MW was from solar photovoltaic (PV) cells. The massive deployment of solar PV technology in Africa to address energy access also poses a challenge of lack of skilled manpower in the sector. What factors drive and influence Africa’s solar sector manpower provision and in what ways are African stakeholders addressing this? Using mixed-method research, interviews (individual and focus group), and published literature and policy documents, we considered Nigeria, Ghana, and Uganda as our focus countries of study. Our study revealed (1) the complexity of the existing regulatory and governance frameworks and how they impact solar skills training, (2) the challenges of greening solar technical and vocational education and training in Africa, and (3) best practices in solar technical education delivery within the African continent that other stakeholders could learn from. We conclude by highlighting how addressing the identified challenges can lead Africa toward a more sustainable energy transition path.

Interfacial forces and nanoconfined environments play crucial roles in transport phenomena within low-dimensional materials. In this study, we propose a photothermally regulated surface-tension mechanism for hydrogen-isotope fractionation, in which light-induced changes in the surface tension of gallium ($\mathrm{Ga}$) dynamically modulate the confined environment between graphene oxide ($\mathrm{GO}$) layers. Under near-infrared irradiation, the surface tension of $\mathrm{Ga}$ decreases, altering its wettability within the interlayer galleries and inducing a reversible contraction of the $\mathrm{GO}$ interlayer spacing. In situ temperature-dependent transmission electron microscopy confirmed this dynamic modulation, showing that confined $\mathrm{Ga}$ nanoparticles undergo fully reversible expansion-contraction and display surface-energy-driven extension behavior analogous to macroscopic particles. Such confinement-induced structural responsiveness regulates the local arrangement of water molecules, leading to distinct molecular aggregation states for ${\mathrm{H}}_2\mathrm{O}$ and ${\mathrm{D}}_2\mathrm{O}$. Due to stronger intermolecular attraction, ${\mathrm{D}}_2\mathrm{O}$ diffusion is more restricted in confinement, leading to pronounced isotope selectivity. Experimentally, the membrane achieved a separation factor of 70.53 for ${\mathrm{H}}_2\mathrm{O}:{\mathrm{D}}_2\mathrm{O}$ and demonstrated selective removal of tritium water (${\mathrm{T}}_2\mathrm{O}$). Our study elucidates the interplay between photothermally driven interfacial energy modulation and confined molecular transport, offering new insights into tunable separation processes and interfacial physics.

Storing hydrogen safely and compactly is a central challenge for clean transportation, grid support, and industrial decarbonization. Metal-organic frameworks (MOFs) are compelling candidates because of their ultrahigh internal surface areas and large pore volumes, yet the design space is effectively infinite, making exhaustive synthesis and simulation impractical. We introduce a physics-informed analytical modeling approach that uses deterministic sparsity-promoting symbolic regression with explicit physical constraints to produce a family of closed-form equations for fast, interpretable, and deployable prediction of usable hydrogen storage in MOFs. On a curated dataset of 88 400 MOFs, the method discovers compact, physically consistent structure-property relationships using as few as one property (void fraction for volumetric capacity) or two properties (void fraction and pore volume for gravimetric capacity) selected from seven descriptors: single-crystal density, gravimetric surface area, volumetric surface area, void fraction, pore volume, largest cavity diameter, and pore-limiting diameter. Equations using one to three properties achieve design-grade accuracy ($R^2$ > 0.99 for gravimetric capacity and $R^2$ > 0.95 for volumetric capacity), while additional descriptors provide marginal gains for high-capacity edge cases, matching black-box machine learning performance and providing mechanistic insights into why some MOFs excel and others do not. Because evaluation is simulation-free and effectively costless, the models integrate directly into high-throughput screening, active learning workflows, and generative design to prioritize syntheses and avoid dead ends, establishing a generalizable path to interpretable and deployable surrogates for adsorption-based energy materials.

Halide vacancies are the dominant point defects in perovskites, with $V_{\mathrm{Cl}}$ identified as a detrimental trap for the optoelectronic performance of ${\mathrm{Cs}\mathrm{Pb}\mathrm{Cl}}_3$, which has applications ranging from photodetectors to solar cells. Understanding these defects under operating conditions is key since their electronic levels exhibit large thermal fluctuations that challenge the validity of static 0 K models. However, quantitative modeling of defect processes requires hybrid density functional theory with spin-orbit coupling, which is too expensive for direct molecular dynamic simulations. To address this, we train a multitask machine learning force field to study $V_{\mathrm{Cl}}$ in orthorhombic ${\mathrm{Cs}\mathrm{Pb}\mathrm{Cl}}_3$ at 300 K. While we observe strong oscillations in the optical transition level arising from the soft potential energy surface, neither the nonradiative capture barriers nor the thermodynamic charge transition levels are affected. Our results reveal that $V_{\mathrm{Cl}}$ is not responsible for the nonradiative losses previously assumed. Instead, its impact on performance arises from other mechanisms, such as limiting the open-circuit voltage and promoting ionic migration. Our findings demonstrate that, despite strong dynamical effects in halide perovskites, the conventional static formalism of defect theory remains valid for predicting thermodynamic behavior, providing a sound basis for the design of high-performance energy materials.

Though rocking-chair batteries are ubiquitously associated with driving electronic devices, descriptions of their dynamical operation have generally been decoupled from the conventional drift-diffusion framework applied to solid-state electronic devices—the latter having enjoyed enormous success in the design of ubiquitous technologies including transistors, solid-state lasers, and light-emitting diodes. Building on long-standing Newman-type descriptions of battery operation, herein we discuss and present a theoretical framework for also conceptualizing the operation of alkali-ion rocking-chair batteries in terms of a competition between ion drift and diffusion via forces and concentration gradients, respectively. It is argued that the key link needed to facilitate a stronger physical bridge between coupled ionic and electronic degrees of freedom lies in the concept of a quasielectric field acting on ions between heterojunctions of ionic materials; a concept borrowed from the solid-state-device literature, where it has been extensively applied in heterojunction devices for decades, and extended in this work via the internal-energy contribution per ion particle. Within a model polymer redox battery system, for which a zero-vacuum reference energy for ions is also proposed, this approach is shown to capture standard battery concepts, including charging, discharging, capacity loss, internal resistances, nonequilibrium charge densities, and operating voltages. In this manner, the findings presented seek to provide a fundamental lasting bridge between electrochemical and electronic device descriptions.

Graphite remains the most widely used anode material in lithium-ion batteries, and the initial formation of the solid electrolyte interphase (SEI) largely dictates the long-term performance of commercial cells. Developing a new graphite anode to diversify the supply chain remains challenging due to proprietary formation protocols and inconsistent test formats. In this study, we evaluate a pitch-coated synthetic graphite developed by James Durrans & Sons against a commercial benchmark using a harmonized methodology across a coin, three-electrode PAT cell, and pilot line built single-layer pouch cells. By standardizing electrode morphology, formation procedures, and testing conditions, we isolate the influence of active material properties, e.g., how the first cycle loss is linked to SEI formation and how coatings and cycling conditions such as temperature can influence it. Under “like for like” comparative studies of NMC 811//graphite pouch cell, the modified synthetic graphite developed displayed higher rate performance and increased capacity retention at 1C over 200 cycles over established commercial counterparts.