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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Violation of detailed balance in non-equilibrium magnons observed by inelastic neutron scattering

Traditional inelastic neutron scattering (INS) characterizes excitations—such as phonons and magnons—in condensed matter systems at thermodynamic equilibrium. However, the most intriguing and puzzling many-body effects in open quantum systems often emerge from dissipative dynamics that are inherently out of equilibrium. Here, we use a combination of laser pumping and INS to experimentally observe long-lived nonequilibrium magnons in a two-dimensional (2D) square-lattice Heisenberg antiferromagnet. These nonequilibrium magnons manifest themselves as a violation of detailed balance in the dynamic structure factor and reach steady states under periodic driving, analogous to nonequilibrium steady states in driven dissipative systems. Furthermore, we show that the violation of detailed balance reflects the quantum-mechanical nature of the underlying dynamical system, where out-of-time-ordered correlations of creation and annihilation operators do not satisfy commutation relations. The in operando INS technique developed here provides a new approach to studying nonequilibrium magnons in prototypical 2D quantum magnets and can be extended to other systems, including one-dimensional spin chains and topological many-body spin systems, where nonequilibrium effects are widespread and rich in discovery potential.

Hua, Chengyun [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2025

Efficient Quantum Gibbs Samplers with Kubo–Martin–Schwinger Detailed Balance Condition

Lindblad dynamics and other open-system dynamics provide a promising path towards efficient Gibbs sampling on quantum computers. In these proposals, the Lindbladian is obtained via an algorithmic construction akin to designing an artificial thermostat in classical Monte Carlo or molecular dynamics methods, rather than being treated as an approximation to weakly coupled system-bath unitary dynamics. Recently, Chen, Kastoryano, and Gilyén (arXiv:2311.09207) introduced the first efficiently implementable Lindbladian satisfying the Kubo–Martin–Schwinger (KMS) detailed balance condition, which ensures that the Gibbs state is a fixed point of the dynamics and is applicable to non-commuting Hamiltonians. This Gibbs sampler uses a continuously parameterized set of jump operators, and the energy resolution required for implementing each jump operator depends only logarithmically on the precision and the mixing time. In this work, we build upon the structural characterization of KMS detailed balanced Lindbladians by Fagnola and Umanità, and develop a family of efficient quantum Gibbs samplers using a finite set of jump operators (the number can be as few as one), akin to the classical Markov chain-based sampling algorithm. Compared to the existing works, our quantum Gibbs samplers have a comparable quantum simulation cost but with greater design flexibility and a much simpler implementation and error analysis. Moreover, it encompasses the construction of Chen, Kastoryano, and Gilyén as a special instance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Best of both worlds: Enforcing detailed balance in machine learning models of transition rates

The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps of the evolution process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where the state-to-state evolution of the system is represented in terms of a continuous-time Markov chain, are instead frequently relied upon to efficiently predict long-time evolution. The accuracy of kMC simulations however relies on the complete and accurate knowledge of reaction pathways and corresponding kinetics. This requirement becomes extremely stringent in complex systems such as concentrated alloys where the astronomical number of local atomic configurations makes the a priori tabulation of all possible transitions impractical. Machine learning models of transition kinetics have been used to mitigate this problem by enabling the efficient on-the-fly prediction of kinetic parameters. While conventional KMC methods based on transition state theory naturally yield reversible dynamics that exactly obey the detailed balance criterion, providing strong guarantees on the properties of the stationary distribution, many recently-proposed ML-based approaches to barrier predictions provide no such guarantees. In this study, we derive conditions under which physics-informed ML architectures exactly enforce the detailed balance condition by construction, even when relying on non-extensive descriptions of states in terms of local environments around mobile defects. In conclusion, using the diffusion of a vacancy in a concentrated alloy as an example, we show that such ML architectures also exhibit superior performance in terms of prediction accuracy, demonstrating that the imposition of physical constraints can facilitate the accurate learning of barriers at no increase in computational cost.

36 MATERIALS SCIENCE↗
DOE OSTI2024

As‐Doped Polycrystalline CdSeTe: Localized Defects, Carrier Mobility and Lifetimes, and Impact on High‐Efficiency Solar Cells

Abstract The efficiency potential for single‐junction photovoltaics (PV) is described by the detailed balance model, which requires the elimination of nonradiative recombination and perfect minority carrier collection. Improvements in GaAs, Si, and perovskite PV follow this model. It might be more complex for CdTe, a leading thin‐film PV technology. While lifetime, passivation, and doping goals for 25% efficient CdTe solar cells are largely reached, voltage is ≈20% below the detailed balance limit. Why is that? In Se‐alloyed CdSe x Te 1‐x (Se is required for >20% efficiency) additional losses can occur due to electrostatic and bandgap fluctuations and due to electronic trap states. To understand mechanisms limiting CdSeTe solar cell performance and to suggest improvements, carrier dynamics, and transport in CdSe x Te 1‐x with variation in Se composition and as doping is analyzed. It is shown that trapping, likely due to anion‐site defects and their complexes, is correlated with low charge carrier mobility of 0.1–0.6 cm 2 (Vs) −1 . Even with 1000 ns charge carrier lifetimes, carrier diffusion length is less than the absorber thickness, reducing efficiency to ≈23%. Device simulations are used to analyze the performance of CdSe x Te 1‐x solar cells; thermodynamic models are not sufficient for absorbers with electronic disorder and trapping.

14 SOLAR ENERGY↗
DOE OSTI2026

Time irreversibility as an indicator of approaching tipping points in Earth subsystems

With shifting environmental trends, many Earth system elements may be poised to undergo critical transitions or ‘tipping’. Reliable anticipation of these tipping elements is vital to inform policy decisions. Many of the current methods for tipping point detection are based on loss of resilience or ‘critical slowdown’ of the system as it approaches a tipping point. However, these methods are prone to false alarms; the detected slowdown may be an artifact of nonstationary noise unrelated to tipping behavior. Here, we explore the efficacy of early warning signs based on a nonequilibrium thermodynamics framework. The model-free detection method relies on the increased intrinsic time-irreversibility due to detailed balance breaking, preceding the onset of tipping or instabilities. We demonstrate that these EWSs are effective for tipping point detection and robust against false alarms due to nonstationary noise, using idealized models for two key elements of the Earth system that are prone to tipping: the Atlantic Meridional Overturning Circulation and Arctic sea-ice loss.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Equilibrium spin polarization arising from chirality

Chirality-induced spin selectivity (CISS) describes how chiral molecules and materials generate spin polarization even at thermal equilibrium. This observation has challenged established principles of microscopic reversibility and Onsager reciprocity. We resolve this paradox by formulating a pseudo-Hermitian quantum framework that separates thermodynamic equilibrium from time-reversal symmetry. Within this approach, structural chirality and electron correlations, irrespective of their microscopic origin, are sufficient to produce CISS observables. Chirality enters through a non-local metric η that couples spin and spatial motion, leading to real spectra, unitary evolution, and thermodynamic consistency. The framework predicts a chirality-induced spin magnetic ordering characterized by a spin-displacement order, which reconciles equilibrium spin polarization with detailed balance and explains the persistence of CISS in materials composed of light elements. We derive generalized Onsager-Casimir relations that respect the observed CISS symmetry, i.e., parity $(\mathscr{P})$-odd and time-reversal $(\mathscr{T})$-odd, but exhibiting $\mathscr{PT}$-even symmetry. This approach establishes a coherent foundation for equilibrium CISS and provides a route to link chemical chirality with measurable spin-to-charge conversion effects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Fundamental advantages of multijunction thermoradiative cells

Thermoradiative (TR) cells convert heat to work through emission of thermal radiation. Multijunction thermoradiative cells have received little research interest due to the apparent overlap with energy harvesting limits. Through detailed balance formalism, the present study models both single- and multi-diode TR devices, establishing their performance limits and identifying key factors that influence efficiency. For single-junction TR cells, we derive a relationship for the optimal bandgap and show that higher emitter temperatures increase efficiency. When the receiver is at absolute zero temperature, multijunction TR cells have little advantage over the single-junction cell in terms of the maximum output power density. However, for higher receiver temperatures, the multijunction configuration demonstrates significant improvements in both power and efficiency through the optimization of the chemical potential and bandgap of each diode in the complete ensemble. A 500 K emitter and 300 K receiver TR system can achieve a 21.25% increase in efficiency and a 10% improvement in the power density with multijunction architecture compared to a single junction through chemical potential optimization. These findings suggest that multijunction TR cells offer a promising approach to advancing low-grade heat recovery technologies for efficient heat-to-work conversion.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

CdTe Core: Final Technical Report (FTR)

CdTe is presently the cost-leading thin-film PV technology, directly competing with Si at scale, even when domestically manufactured. While an impressive technology, its efficiency remains much below the detailed balance limit with the largest cause due to its low photovoltage and fill factor. To realize gains, the carrier concentration, minority carrier lifetime, and interface recombination all need to be improved simultaneously over historic levels. Using a new defect chemistry (group V doping instead of copper) has been identified as a viable route using single crystal systems. This project focused on implementing this new defect chemistry in scalable, polycrystalline thin-film photovoltaic CdTe devices with tasks focusing improvements to the front interface, absorber, and rear interface as well as capability development & stakeholder engagement. The goal of the project was to establish a strategy using devices, test structures, detailed characterization, and modeling to quantify the sources of losses in state-of-the-art CdTe photovoltaic devices. Using this strategy and advanced synthesis, losses at the front interface, absorber, and rear interface were worked on in parallel. The final objective was to significantly improve the voltage deficit in CdTe devices to enable improvements in photovoltage and efficiency that can be implemented by industry in the near-term. Over the course of the project, the team developed new characterization techniques, analysis, and modeling which were then applied to state-of-the-art materials generated internally and collaboratively. In particular to enable rapid progress, NREL worked closely with First Solar where NREL grew complete devices as well as ones that interleaved process steps where First Solar had completed different steps such as absorber growth or absorber growth and activation using their baseline methods. Using detailed characterization and analysis including photoemission, photoluminescence, and scanning probe techniques enabled understanding of the loss pathways and area for improvements in our own and First Solar s materials. Ultimately, this contributed to the first series of new world record CdTe efficiencies since 2016, culminating in a 23.1% certified cell that was P-doped along with As-doped cells of similar performance. Internally, NREL improved the statistical variation in baseline As-doped devices and improved average photovoltage by over 100 mV. This was done through an improvement in absorber quality, changed front interface, and improved back contact. In addition to materially improving the fabrication processes at NREL, characterization, analysis, and modeling were developed and disseminated. NREL also played a pivotal role in community building over the course of this project working closely with the Cadmium Telluride Accelerator Consortium. NREL worked in a series of collaborations with academic and industry partners, leveraging knowledge and innovations from this project, as well as helped organize a series of workshops to ensure rapid progress in the field. Working closely with the academic community has led to a dissemination of knowledge; working with First Solar as increased US competitiveness First Solar expanded domestic production to ~10 GW and opened new facilities.

14 SOLAR ENERGY↗
DOE OSTI2026

General framework for quantifying dissipation pathways in open quantum systems. III. Off-diagonal subsystem–bath couplings

This paper extends the previously reported theory of dissipation pathways [C. W. Kim and I. Franco, J. Chem. Phys. 160, 214111 (2024)] to incorporate off-diagonal subsystem–bath coupling, which is often required to model molecular systems where the environment directly influences transitions and couplings between subsystem states. We systematically derive master equations for both population transfer and dissipation into individual bath components, for which we also rigorously prove energy conservation and detailed balance. The approach is based on second-order perturbation theory with respect to the subsystem–bath couplings, whose form is not limited to any specific model. The accuracy of the developed method is tested by applying it to diverse model Hamiltonians involving linearly coupled harmonic oscillator baths and comparing the outcomes against the hierarchical equations of motion (HEOM) method. Overall, our method accurately quantifies the contributions of specific bath components to the overall dissipation while significantly reducing the computational cost compared to numerically exact methods such as HEOM, thus offering a path to examine how vibronic interactions steer non-adiabatic processes in realistic chemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Coupling to rotational manifolds to improve gas-phase pump–probe spectroscopic models

The physical picture of gas-phase optical transitions is normally presented as an isolated two-level system balanced by upward and downward processes. Isolated models assume a phenomenological treatment of collisional dephasing but do not strictly account for collisional population exchange with the rotational baths. While this assumption is valid under low-intensity conditions, where excitation is rate-limiting, isolated models can deviate from Beer’s Law at sufficient pressures and monochromatic intensities when both collisional broadening and power broadening are comparable to (or greater than) lifetime broadening, which are not uncommon conditions for cavity enhanced spectroscopies in the mid-IR spectral range. Although this problem has been addressed by rate-equation models for linear absorption measurements, a general treatment for multi-level quantum mechanical models suitable for non-linear absorption measurements (two-photon/two-color/pump–probe) is lacking. Isolated models require physical parameter inputs that disagree with expected values by at least an order of magnitude. These non-physical models undermine the ability to predict non-linear signal strengths under untested conditions and thereby limit the potential to optimize the sensitivity of non-linear spectroscopies and to expand their analytical applications (e.g., new analytes and/or buffer gases, changes in cavity free-spectral-range, changes in intracavity powers or wavelengths, and accurate investigation of physical phenomena). In this study, we derive bath-coupled models for gaseous pump–probe spectroscopy by application of the quantum Lindblad equation and detailed balance. Bath-coupled models are shown to fit data consistently across variations in intensity and agree with all physically expected values.

Cavity ring-down spectroscopy↗
DOE OSTI2025

Accelerating multilevel Markov Chain Monte Carlo using machine learning models

Here, this work presents an efficient approach for accelerating multilevel Markov Chain Monte Carlo (MCMC) sampling for large-scale problems using low-fidelity machine learning models. While conventional techniques for large-scale Bayesian inference often substitute computationally expensive high-fidelity models with machine learning models, thereby introducing approximation errors, our approach offers a computationally efficient alternative by augmenting high-fidelity models with low-fidelity ones within a hierarchical framework. The multilevel approach utilizes the low-fidelity machine learning model (MLM) for inexpensive evaluation of proposed samples thereby improving the acceptance of samples by the high-fidelity model. The hierarchy in our multilevel algorithm is derived from geometric multigrid hierarchy. We utilize an MLM to accelerate the coarse level sampling. Training machine learning model for the coarsest level significantly reduces the computational cost associated with generating training data and training the model. We present an MCMC algorithm to accelerate the coarsest level sampling using MLM and account for the approximation error introduced. We provide theoretical proofs of detailed balance and demonstrate that our multilevel approach constitutes a consistent MCMC algorithm. Additionally, we derive the expression for cost reduction due to machine learning model to facilitate cost analysis of the hierarchical sampling algorithm. Our technique is demonstrated on a standard benchmark inference problem in groundwater flow, where we estimate the probability density of a quantity of interest using a four-level MCMC algorithm. Our proposed algorithm accelerates multilevel sampling by a factor of two while achieving similar accuracy compared to sampling using the standard multilevel algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

15.3% AM1.5G Efficiency GaAs Solar Cells Fabricated via an Epitaxy-Free Process

Here, we report simple and potentially low-cost techniques for creating high-quality n-type gallium arsenide (GaAs) and GaAs p/n junctions and fabricate GaAs p/n junction solar cells. Detailed-balance modeling suggests that 20% AM1.5G efficiency p/n homojunction devices may be possible if the surface doping concentration can be limited to values less than ∼ 5 × 10 19 cm −3 . Our process exploits an open-tube, vapor-phase, deposition-free, zinc diffusion technique for forming p-type layers in melt-grown n-GaAs substrates that results in sheet resistances less than 1 kΩ/$\square$. In addition, we have improved the minority carrier diffusion lengths of melt-grown GaAs from less than one micron to over five microns using an open-tube, vacuum-free, annealing process which reduces the density of EL2 midgap defects. Finally, we have combined these advances to fabricate epitaxy-free, GaAs solar cells with a validated AM1.5G efficiency of 15.3%.

14 SOLAR ENERGY↗
DOE OSTI2026

Accelerating multicanonical sampling with irreversibility

Flat-histogram Monte Carlo simulations are well-established, robust methods to perform random walks in a physical observable or parameter space, making them suitable for finding ground states or studying phase transitions in complex systems in statistical physics. However, their efficiency can be limited by the time to attain the desired flat distribution, which is generally unknown prior to the simulations. In particular, they might suffer from slowing down towards the end of a simulation due to the diffusive nature of random walks. In this work we apply irreversibility to the multicanonical Monte Carlo method via the lifting approach to alleviate this behavior. We achieve a 2–4 times speedup in ground-state search for a two-dimensional (2D) Ising model, and up to an order of magnitude of speedup for finding the ground-state energy in an Edwards–Anderson spin glass, compared to traditional multicanonical sampling. In conclusion, the round-trip times between ground states show a narrower distribution and are significantly shorter compared to the reversible counterpart, suggesting that a lower convergence time with a smaller time variance is feasible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Conductivity Spectroscopy for Investigation and Discovery of Photovoltaic Materials

Conductivity spectroscopy is an extremely powerful set of methods for probing the properties of optoelectronic materials, especially photovoltaics, where photoconductivity is one of the best spectroscopic proxies for performance. Despite this power, they are substantially less commonly used than time-resolved photoluminescence (for instance) because they tend to be more expensive to implement (THz) and/or require specialized knowledge (GHz) to construct instruments, which are not widely available. The goal of this review is to illustrate the utility of these experiments in the discovery and study of photovoltaic absorber materials and simultaneously make them more accessible to the community by providing a central tutorial resource. We provide a comprehensive review of how conductivity spectroscopy has developed over the past decade and been applied in the discovery and development of photovoltaic materials, with a primary focus on emerging solution-processable technologies. Along the way we aim to demystify conductivity spectroscopy with focused tutorial sections that explain the physical models used to fit the data and illustrate how to think about “high-frequency conductivity”.

14 SOLAR ENERGY↗
DOE OSTI2026

Hydrogen Production Cost from Proton-Conducting Solid Oxide Electrolysis

Rigorous stakeholder-vetted techno-economic analysis (TEA) was conducted to estimate the cost of hydrogen (H 2 ) production using Proton-Conducting Solid Oxide (PSO) electrolysis. The analysis evaluates Current (2025) and Future (2035) technology cases at centralized plant scales of 50 and 500 metric tonnes per day (MTD), assuming electricity, water, and air as the only system inputs. Untaxed, unsubsidized levelized cost of hydrogen (LCOH) is projected to range from 2020 $\$$1.81 to $\$$2.47/kg H 2 at an electricity price of $\$$0.03/kWh and 97% capacity factor under Nth-of-a-kind (NOAK) deployment assumptions. Total installed capital cost was developed using bottom-up Design for Manufacture and Assembly (DFMA) stack cost modeling and detailed balance-of-plant estimates, including mechanical and electrical subsystems, installation, site preparation, engineering, and contingency. Stack performance assumptions include thermoneutral operation, degradation over time, and periodic replacement. LCOH was calculated using the Hydrogen Analysis (H2A) discounted cash flow model in constant 2020 dollars. Results indicate PSO electrolysis has potential for competitive hydrogen production costs under low-cost electricity and mature manufacturing conditions.

08 HYDROGEN↗
DOE OSTI2025

Quantitative decoding of coupled carbon and energy metabolism in Pseudomonas putida for lignin carbon utilization

Soil Pseudomonas species, which thrive on lignin derivatives, are widely explored for biotechnology applications in lignin valorization. However, how the native metabolism coordinates phenolic carbon processing with required cofactor generation remains poorly understood. Here, we achieve quantitative understanding of this metabolic balance through a detailed multi-omics investigation of Pseudomonas putida KT2440 grown on four common phenolic acid substrates: ferulate, p-coumarate, vanillate, and 4-hydroxybenzoate. Relative to succinate, proteomics reveals > 140-fold increase in transport and catabolic proteins for aromatics, but metabolomics identifies bottlenecks in initial catabolism to maintain favorable cellular energy charge, which is compromised in mutants with resolved bottlenecks. Up to 30-fold increase in pyruvate carboxylase and glyoxylate shunt proteins implies a metabolic remodeling confirmed by kinetic 13 C-metabolomics. Quantitative analysis by 13 C-fluxomics demonstrates coupling of this remodeling with cofactor production. Specifically, anaplerotic carbon recycling through pyruvate carboxylase promotes tricarboxylic acid cycle fluxes to generate 50-60% NADPH yield and 60-80% NADH yield, resulting in up to 6-fold greater ATP surplus than with succinate metabolism; the glyoxylate shunt sustains cataplerotic flux through malic enzyme for the remaining NADPH yield. This quantitative blueprint affords cofactor imbalance predictions in proposed engineering of key metabolic nodes in lignin valorization pathways.

09 BIOMASS FUELS↗
DOE OSTI2025

SwinCell: a 3D transformer and flow-based framework for improved cell segmentation

Segmentation of three-dimensional (3D) cellular images is fundamental for studying and understanding cell structure and function. However, 3D cellular segmentation is challenging, particularly for dense cells and tissues. This challenge arises mainly from the complex contextual information within 3D images, anisotropic properties, and the sensitivity to internal cellular structures, which often lead to incorrect segmentation. In this work, we introduce SwinCell, a 3D transformer-based framework that leverages Swin-transformer to predict flow and differentiate individual cell instances. We demonstrate SwinCell’s utility in the segmentation of nuclei, colon tissue cells, and densely cultured cells. SwinCell strikes a balance between maintaining detailed local feature recognition and understanding broader contextual information. Through extensive testing with both public and in-house 3D cell imaging datasets, SwinCell shows utility in segmenting dense cells, making it a valuable tool for 3D segmentation in cellular analysis that could expedite research in cell biology and tissue engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

FC-PLACER (Fuel Cell Plant Layout and Cost Estimation Resource) [SWR-26-027]

The Fuel Cell Plant Layout and Cost Estimation Resource (FC-PLACER) is a tool to perform a footprint and cost analysis for hydrogen fuel cell based power plants. This analysis tool provides a comprehensive design and cost assessment for a 100-MW stationary PEM fuel cell power plant, utilizing specifications from commercially available PEM fuel cell modules originally designed for heavy-duty vehicle applications. Additionally, the tool offers flexibility, enabling adaptation to various capacity requirements or plant configurations and facilitating the evaluation of system layout and overnight costs. In particular, it includes a detailed accounting of balance of plant material and labor costs and enables a precise estimate of plant spatial footprint.

Reznicek, Evan [National Laboratory of the Rockies↗