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DOE OSTI2026

Runoff evaluation in an Earth System Land Model for permafrost regions in Alaska

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce. In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-based integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM's parameterized representation of total runoff. Results show that (1) ELM's total runoff was the same order of magnitude as the ATS simulations, and both models were similarly variable over time; (2) minor adjustments to coefficients in ELM's runoff parameterization improved the match between the ATS simulation and ELM's parameterized representation of annual and seasonal total runoff; (3) overall, runoff responses in ATS and ELM are more similar in flat hillslope environments compared to steep hillslopes; and (4) shallower active layer thicknesses and higher precipitation simulations resulted in lower correlations between the two models due to greater total runoff. By incorporating the optimized runoff coefficients from the Sagavanirktok River basin into ELM, the simulated total runoff better matched the streamflow observations at a small watershed located on the Seward Peninsula of Alaska. Our findings revealed important insights into the effectiveness of runoff parameterizations in land surface models and pathways for improving runoff coefficients in typical Arctic regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Utah FORGE: Interferometric Synthetic Aperture Radar Data from 2023 and 2024

The dataset comprises Interferometric Synthetic Aperture Radar (InSAR) data from the TerraSAR-X and TanDEM-X satellite missions, covering the Utah FORGE site. This data includes interferometric pairs created using GMT-SAR processing software, chosen for their short orbital separations between May 1, 2023, and June 30, 2024. Included are various data and metadata, including Digital Elevation Models, unit vectors, and correlation coefficients. The dataset is packaged in several compressed tar files and formatted in NetCDF. To utilize this dataset, users will need software capable of handling NetCDF files and tools for decompressing tar files.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Variable X-Ray Reverberation in the Rapidly Accreting Active Galactic Nucleus Ark 564: The Response of the Soft Excess to the Changing Geometry of the Inner Accretion Flow

X-ray reverberation, which exploits the time delays between variability in different energy bands as a function of Fourier frequency, probes the structure of the inner accretion disks and X-ray coronae of active galactic nuclei. We present a systematic X-ray spectroscopic and reverberation study of the high-Eddington-ratio narrow-line Seyfert 1 galaxy Ark 564, using over 900 ks of XMM-Newton and NuSTAR observations spanning 13 yr. The time-averaged spectra can be well described by a model consisting of a coronal continuum, relativistic disk reflection, warm Comptonization, and three warm absorbers. Leveraging the high X-ray brightness of Ark 564, we are able to resolve the time evolution of the spectra and contemporaneous reverberation lags. The soft-band lag relative to the continuum increases with the X-ray flux, while Fe Kα lags are detected in only a subset of epochs and do not correlate with soft lags. Models based on a lamppost corona and reflection from a standard thin disk can broadly reproduce the observed lag-energy spectra of low-flux epochs; however, additional reverberation from the warm Comptonized atmosphere is required to explain the soft lags observed in high-flux epochs. A vertically puffed-up inner disk and a variable, vertically extended corona can better explain the observed evolution of the lags and covariance spectra. Our study underscores the importance of multiepoch, multiband analyses for a comprehensive understanding the inner accretion disk and corona.

Active galactic nuclei↗

DOE OSTI2024

The DEHVILS in the details: Type Ia supernova Hubble residual comparisons and mass step analysis in the near-infrared

Measurements of type Ia supernovae (SNe Ia) in the near-infrared (NIR) have been used both as an alternate path to cosmology compared to optical measurements and as a method of constraining key systematics for the larger optical studies. With the DEHVILS sample, the largest published NIR sample with consistent NIR coverage of maximum light across three NIR bands ( Y, J , and H ), we check three key systematics: (i) the reduction in Hubble residual scatter as compared to the optical, (ii) the measurement of a “mass step” or lack thereof and its implications, and (iii) the ability to distinguish between various dust models by analyzing slopes and correlations between Hubble residuals in the NIR and optical. We produce SN Ia simulations of the DEHVILS sample and find that it is harder to differentiate between various dust models than previously understood. Additionally, we find that fitting with the current SALT3-NIR model does not yield accurate wavelength-dependent stretch-luminosity correlations, and we propose a limited solution for this problem. From the data, we see that (i) the standard deviation of Hubble residual values from NIR bands treated as standard candles are 0.007–0.042 mag smaller than those in the optical, (ii) the NIR mass step is not constrainable with the current sample size of 47 SNe Ia from DEHVILS, and (iii) Hubble residuals in the NIR and optical are correlated in the data. We test a few variations on the number and combinations of filters and data samples, and we observe that none of our findings or conclusions are significantly impacted by these modifications.

Astronomy & Astrophysics↗

DOE OSTI2024

Quasi-Lindblad pseudomode theory for open quantum systems

Here, we introduce a new framework to study the dynamics of open quantum systems with linearly coupled Gaussian baths. Our approach replaces the continuous bath with an auxiliary discrete set of pseudomodes with dissipative dynamics, but we further relax the complete positivity requirement in the Lindblad master equation and formulate a quasi-Lindblad pseudomode theory. We show that this quasi-Lindblad pseudomode formulation directly leads to a representation of the bath correlation function in terms of a complex weighted sum of complex exponentials, an expansion that is known to be rapidly convergent in practice and thus leads to a compact set of pseudomodes. The pseudomode representation is not unique and can differ by a gauge choice. When the global dynamics can be simulated exactly, the system dynamics is unique and independent of the specific pseudomode representation. However, the gauge choice may affect the stability of the global dynamics, and we provide an analysis of why and when the global dynamics can retain stability despite losing positivity. We showcase the performance of this formulation across various spectral densities in both bosonic and fermionic problems, finding significant improvements over conventional pseudomode formulations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Concurrent Inter-Model Spread of Boreal Winter Westerly Jet Meridional Positions Between the Northern and Southern Hemispheres in CMIP6 Models

Here, this study investigates the inter-model spread of climatological extratropical westerly jets in boreal winter, using the historical simulation of 52 Coupled Model Intercomparison Project phase 6 (CMIP6) models from 1851 to 2014. The results show that there is a substantial spread in the latitude of the upper-tropospheric westerly jet across models, characterised by large inter-model standard deviations to both the poleward and equatorward sides of the jet axis, although the multi-model ensemble mean (MME) performs well in simulating meridional position of westerly jets. Furthermore, we detect the consistency of inter-model jet position spread between the Northern and Southern Hemispheres, based on the inter-model empirical orthogonal function (EOF) decomposition and correlation of regional-averaged zonal winds. Specifically, the models that simulate the westerly jets poleward/equatorward relative to the MME position in one hemisphere also tend to simulate the jets poleward/equatorward in the other hemisphere. Accordingly, we define a global jet spread index to depict the concurrence of jet shift in the two hemispheres. The results of inter-model regression analyses based on this index indicate that the models positioning the jets poleward than the MME tend to simulate a wider Hadley Cell, a poleward-shifted Ferrel Cell in the Southern Hemisphere, enhanced precipitation in the subtropics and suppressed precipitation in the tropics, and warmer sea surface temperatures in the subtropics and mid-latitudes. The present results suggest that improving the simulation of jet positions in climate models requires a comprehensive consideration of thermal states in the tropics and subtropics/mid latitudes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Microstructure Scale Lithium-Ion Battery Modeling: Part III. When and Where Lithium Plating Occurs and its Correlation with the Electrode Microstructure

Li-ion battery performance and degradation are closely related to the cell’s underlying electrode microstructure. Electrode microstructures are typically characterized with volume-averaged properties that neglect the impact of local heterogeneities. However, local heterogeneities create hot spots that can trigger degradation onset. Herein, a microstructure scale electrochemical model is used to investigate the impact of microstructure heterogeneity on lithium plating. The model predicts lithium plating is not uniform, even when considering a relatively small portion of the electrode (a cross-sectional area of 154×144 µm 2 ), preferring to plate on larger particles as compared to smaller particles. While local heterogeneities control where plating occurs, the model predicts that volume-averaged properties control when plating occurs. Additionally, the model predicts that the active material specific surface area has a linear relationship with the plating onset. However, the linear relationship between increased active material surface area and delayed plating response appears to be sensitive to the microstructure feature used to increase the active interface area. Here, a comparative case-study is explored where the specific surface area is increased by either reducing the active material particle diameter, adding open-porosity cracks, or increasing the active material surface roughness. The model predicts that increasing the specific surface area by reducing the active material particle diameter is the most effective strategy for delaying lithium plating. At 6C, reducing particle size is shown to be 3 and 20 times more effective than, respectively, adding open-porosity cracks and increasing surface roughness. A dual-layer electrode architecture combining gradations both for average properties and uniformities is eventually proposed to improve homogeneous material utilization and reduce degradation at high charge rates.

25 ENERGY STORAGE↗

DOE OSTI2025

Thermodynamic modeling of aqueous acetic acid, butyric acid, and lactic acid solutions

Based on the activity coefficient – fugacity coefficient approach, a rigorous thermodynamic modeling study is presented for accurate correlation of vapor-liquid equilibrium data of aqueous solutions of acetic acid (293 to 391 K), butyric acid (325 to 436 K), lactic acid (378 to 409 K), and acetic acid + butyric acid binary mixture (358 to 421 K). In addition, the pH data of the three aqueous, single carboxylic acid solutions were measured at 298 to 328 K and successfully correlated. Given that these aqueous carboxylic acid solutions exhibit various degrees of association behavior in both vapor and liquid phases, the thermodynamic models considered for this study include the Redlich-Kwong equation of state (RK-EoS) and the Hayden-O’Connell equation of state (HOC-EoS) for the vapor phase fugacity coefficients and the electrolyte non-random two-liquid model (eNRTL) and the association electrolyte non-random two-liquid model (AeNRTL) for the liquid phase activity coefficients. The combination of the HOC-EoS for the vapor phase and the AeNRTL model for the liquid phase is found to provide the best correlation results, consistent with the fact that the HOC-EoS and the AeNRTL model explicitly account for association behaviors in the vapor phase and the liquid phase, respectively.

09 BIOMASS FUELS↗

DOE OSTI2024

Performance Prediction of High‐Entropy Perovskites La 0.8 Sr 0.2 Mn x Co y Fe z O 3 with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition–structure–performance relationships of high-entropy La 0.8 Sr 0.2 Mn x Co y Fe z O 3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion—derived from spectral analysis of Raman-active modes—and enhanced performance.

high entropy oxides↗

DOE OSTI2026

Multireference Embedding and Fragmentation Methods for Classical and Quantum Computers: From Model Systems to Realistic Applications

One of the primary challenges in quantum chemistry is the accurate modeling of strong electron correlation. While multireference methods effectively capture such correlation, their steep scaling with system size prohibits their application to large molecules and extended materials. Quantum embedding offers a promising solution by partitioning complex systems into manageable subsystems. In this Review, we highlight recent advances in multireference density matrix embedding and localized active space self-consistent field approaches for complex molecules and extended materials. We discuss both classical implementations and the emerging potential of these methods on quantum computers. Here, by extending classical embedding concepts to the quantum landscape, these algorithms have the potential to expand the reach of multireference methods in quantum chemistry and materials.

Algorithms↗

DOE OSTI2025

Identifying Band Inversions in Topological Materials Using Diffusion Monte Carlo

Topological insulators are characterized by insulating bulk states and robust metallic surface states. Band inversion is a hallmark of topological insulators. At time-reversal invariant points in the Brillouin zone, spin–orbit coupling (SOC) induces a swapping of orbital character at the bulk band edges. Reliably detecting band inversion in solid-state systems with many-body methods would aid in identifying possible candidates for spintronics and quantum computing applications and improve our understanding of the physics behind topologically nontrivial systems. Density functional theory (DFT) methods are a well-established means of investigating these interesting materials due to their favorable balance of computational cost and accuracy but often struggle to accurately model the electron–electron correlations present in the many materials containing heavier elements. In this work, we develop a novel method to detect band inversion within continuum quantum Monte Carlo (QMC) methods that can accurately treat the electron correlation and spin–orbit coupling that are crucial to the physics of topological insulators. Our approach applies a momentum-space-resolved atomic population analysis throughout the first Brillouin zone utilizing the Löwdin method and the one-body reduced density matrix produced with diffusion Monte Carlo (DMC). We integrate this method into QMCPACK, an open source ab initio QMC package, so that these ground-state methods can be used to complement experimental studies and validate prior DFT work on predicting the band structures of correlated topological insulators. Here, we demonstrate this new technique on the topological insulator bismuth telluride, which displays band inversion between its Bi-p and Te-p states at the Γ-point. We show an increase in charge on the bismuth-p orbital and a decrease in charge on the tellurium-p orbital when comparing band structures with and without SOC. Additionally, we use our method to compare the degree of band inversion present in monolayer Bi 2 Te 3 , which has no interlayer van der Waals interactions, to that seen in the bilayer and bulk. The method presented here will enable future many-body studies of band inversion that can shed light on the delicate interplay between correlation and topology in correlated topological materials.

Band structure↗

DOE OSTI2025

Thermal Weight Determination and Interstate Coupling in State-Averaged ADAPT-VQE

Characterizing electronic thermal states at low temperatures is an important but challenging task in quantum chemistry and condensed matter physics, making it a prime candidate for a useful application in quantum computing. One of the most successful methods for state preparation on quantum computers is the Adaptive, Problem-Tailored (ADAPT) Variational Quantum Eigensolver (VQE), which has recently been generalized to treat excited states within a state-averaged framework as well as Gibbs states. In this work, we introduce Helmholtz-Optimized Thermal (HOT) ADAPT-VQE, an ancilla-free strategy for preparing Gibbs states that directly minimizes the Helmholtz free energy by targeting the dominant eigenstates of the thermal ensemble. We demonstrate the usefulness of HOT-ADAPT-VQE by predicting the free energy of two model systems with strongly correlated ground states: (1) the Fe 2+ cation in a magnetic field and (2) a [Cu 2 O 7 ] 10– fragment of the Mott insulator La 2 CuO 4 . Our results demonstrate that HOT-ADAPT-VQE significantly improves upon Gibbs-state estimates from multistate variants of ADAPT-VQE, often with substantially shallower quantum circuits, making it a promising candidate for thermal-state calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Interconnection of Aerosol‐Cloud Interactions and Cloud Feedback Through Warm Rain Process

Recent research has revealed a correlation within the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations: models exhibiting more pronounced warming due to positive cloud feedback simultaneously show enhanced cooling from aerosol‐cloud interactions (ACI). However, the interplay between ACI and cloud feedback is not well understood in these models. Our study addresses this gap by modifying the autoconversion rate in two Earth system models (ESMs), elucidating how ACI could connect with cloud feedback through the warm rain process. We find that higher autoconversion rates, which are associated with stronger ACI, suppress the increase in cloud liquid water path (LWP) and cloud optical depth with warming by enhancing the precipitation efficiency over the extratropical regions, leading to a larger positive cloud feedback. This study offers new insights into the compensatory mechanism between ACI and cloud feedback through the warm rain process and highlights the importance of constraining the autoconversion parameterization in models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Redox gating-induced modulation of charge carrier density and lattice expansion in LaNiO 3 thin films

Redox gating involves the use of reversible redox functionalities combined with ionic electrolytes to substantially alter the charge carrier density in functional condensed materials. This modification leads to the emergence of physical properties not observed in the original material. Here, in our study, we focus on redox gating applied to a LaNiO 3 (001) film within a field-effect device and identify a critical gate voltage of 0.7 V. Hall measurements indicate that redox gating markedly increases the charge carrier density in LaNiO 3 , reaching over 10 14 cm −2 . This increase is primarily due to the injection of electrons into LaNiO 3 , which offsets the existing hole carriers. These adjustments in the carrier concentration result in reversible lattice expansion in LaNiO 3 when gate voltages are below 0.7 V. This expansion correlates well with theoretical models that consider adjustments to the Ni–O bond length, influenced by oxygen ligand holes. Conversely, at gate voltages above 0.7 V, there are significant changes in resistivity, lattice structure, and Ni valence, stemming from the formation of oxygen vacancies in the LaNiO 3 film.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Obtaining bulk-like correlated oxide surfaces with protective caps

Functional oxides exhibit a diverse range of correlated electron phenomena, some of which are highly attractive for novel electronic, magnetic, and optical devices. Despite decades of advancement of our fundamental understanding of these materials, they consistently fall short of realizing their promise in functional devices. We identify a significant bottleneck toward device realization to be surface overoxidation. Protective caps can effectively prevent overoxidation, but their interfaces with functional oxides are not well understood. These interfaces are critical for effectively using functional oxides in field-effect devices, where “the interface is the device.” This work addresses the chemistry and physics of the interface between protective caps and the correlated metal SrVO3, a model functional oxide. Our comparison of five different cap materials reveals effective protection and similar SrVO3 surface chemistry in all cases. Systematic comparisons of surface and bulk-sensitive photoelectron spectra reveal that negligible interface redox takes place, elucidating the cap-SrVO3 interface chemistry. This work demonstrates a robust and simple solution to the surface overoxidation problem in vanadates, paving the way toward effectively using these materials in field-effect devices. Our conclusions are general and can be applied to numerous other systems, thus moving oxide electronics closer to the realization of functional devices.

Cohen, Amit (ORCID:0009000276477510)↗

DOE OSTI2024

Studying the interaction between charm and light-flavor mesons

The two-particle momentum correlation functions between charm mesons ( D * ± and D ± ) and charged light-flavor mesons ( π ± and K ± ) in all charge combinations are measured for the first time by the ALICE Collaboration in high-multiplicity proton–proton collisions at a center-of-mass energy of s = 13 TeV . For DK and D * K pairs, the experimental results are in agreement with theoretical predictions of the residual strong interaction based on quantum chromodynamics calculations on the lattice and chiral effective field theory. In the case of D π and D * π pairs, tension between the calculations including strong interactions and the measurement is observed. For all particle pairs, the data can be adequately described by Coulomb interaction only, indicating a shallow interaction between charm and light-flavor mesons. Finally, the scattering lengths governing the residual strong interaction of the D π and D * π systems are determined by fitting the experimental correlation functions with a model that employs a Gaussian potential. The extracted values are small and compatible with zero. © 2024 CERN, for the ALICE Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Antiprotons and Elementary Particles over a Solar Cycle: Results from the Alpha Magnetic Spectrometer

We present results over an 11-year Solar cycle of cosmic antiprotons based on 1.1 × 10 6 events in the rigidity range from 1.00 to 41.9 GV. The $\bar{𝑝}$ fluxes exhibit distinct properties. The magnitude of the $\bar{𝑝}$ flux temporal variation is significantly smaller than those of 𝑝, 𝑒 − , and 𝑒 + . A hysteresis between the $\bar{𝑝}$ fluxes and the 𝑝 fluxes is observed, whereas the $\bar{𝑝}$ and 𝑒 − fluxes show a linear correlation. With a model-independent analysis, we found a universal relation between the shape of the rigidity spectrum and the magnitude of flux temporal variation over an 11-year Solar cycle for both positively and negatively charged particles. The simultaneous results on $\bar{𝑝}$ and 𝑝, 𝑒 − , and 𝑒 + provide unique information for understanding particle transport in the Solar System as a function of mass, charge, and spectral shape.

cosmic ray composition & spectra↗