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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication "Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations". We used field observations of biomass and leaf traits (Salmon et al., 2019b) across a gradient of plant communities on the Seward Peninsula in northwest Alaska (Breen et al., 2020; Iversen et al., 2017) to replace the original ELM configuration with nine, arctic-specific PFTs. Original arctic PFTs in the model were (1) broadleaf deciduous boreal shrub and (2) C3 arctic grass. New PFTs were (1) lichen, (2) bryophyte, (3) evergreen dwarf shrub, (4) deciduous dwarf shrub, (5) deciduous low shrub, (6) deciduous low to tall shrub, (7) alder shrub, (8) forb, and (9) graminoid. New PFTs were parameterized and evaluated using site-level trait and biomass measurements (Salmon et al., 2019b), root C:N ratios and rooting depth patterns (Salmon et al., 2019c), and root biomass (Salmon et al., 2020). This archive contains forcing data and model output from ELM simulations conducted at the Kougarok Hillslope site (Kougarok Road Mile Marker 64). Simulations were conducted for the Kougarok Hillslope site using meteorological driving data from the Scenarios Network for Alaska and Arctic Planning (SNAP) downscaled climate projection dataset using NCAR-CCSM forcing (Bieniek et al., 2020 dataset; Walsh et al., 2018). The archive includes three versions of model simulations representing a progression from the original model configuration to an Arctic-specific configuration (see Output section below). Simulation 1 used grid cell data from a global E3SM configuration, including the fractional area of E3SM PFTs assigned to the grid cell containing the Kougarok Hillslope site in global simulations. This simulation did not distinguish between different plant communities on the Kougarok Hillslope, but instead used a single point simulation to represent the entire area. Simulation 2 used default E3SM PFT definitions combined with adjusted depth to bedrock for plant communities with shallow rocky layers to represent the role of abiotic soil factors in driving site differences (based on depth measurements of Iversen et al., 2019a), and adjusted the relative areas of the broadleaf deciduous boreal shrub and C3 arctic grass PFTs to reflect the observed spatial coverage of shrub and non-shrub PFTs across Kougarok Hillslope plant communities. Simulation 3 used the new arctic PFT definitions and parameterizations based on the vegetation types present at the site, along with the adjusted soil depths used in Simulation 2. Included are *.pdf, *.nc (NetCDF), *tar.gz, *.py, and *.bash files. We would like to thank Mary's Igloo Native Corporation for allowing us to perform this research on their land and giving us the opportunity to share this research with their community. Thanks to Shawn Serbin and Alistair Rogers for helpful discussions. Holly Vander Stel, Joanne Childs, and Stan Wullschleger helped with collection of field data. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Coupled Aerodynamic and Hydrodynamic Hybrid Simulation of Floating Offshore Wind Turbines

The development and innovation of floating offshore wind energy in the U.S. requires detailed high-fidelity observations and measurements of turbine and platform loading due to wind, waves, and currents. However, full-scale and quasi-full-scale experiments require significant financial and temporal investments for construction, experimental testing, and long-term field campaigns. To support the commercial advancement of the offshore wind energy industry, specialized wind tunnel and wave basin experimental facilities are critical to be able to test FOWT designs at small scale under controlled conditions prior to full-scale deployment. Oregon State University (OSU) is internationally known as a leader in water and energy research, development, and testing. The O.H. Hinsdale Wave Research Laboratory (HWRL) and the Wallace Energy Systems and Renewables Facility (WESRF) at OSU have extensive experience building, modeling, monitoring, controlling, and actuating scaled systems. Experiments on wave-structure interaction have been performed at the HWRL since its establishment in 1972. Studies have included the interaction of waves with coastal structures (breakwaters, seawalls, buildings, cylinders, bridges, fixed foundations of offshore wind turbines, etc.) and with floating structures (e.g., wave energy converters, maneuvering of vessels, etc.). Hinsdale is actively used by marine energy technology developers, both for private testing and OSU-collaborative research projects. However, despite the availability of several large-scale facilities for hydrodynamic testing (at OSU and elsewhere in the U.S.), existing experimental laboratories are generally limited in their ability to accurately generate combined wind and wave conditions. The simulation of both wind and waves in experimental testing is complicated due to a number of constraints, including: [i] incompatible similitude laws governing the wind and waves for scaled experiments, [ii] producing accurate wind over a large enough control volume via fans, and [iii] generating wind that reasonably represents the atmospheric boundary layer in existing wave basins/flumes. Hence, physical test data providing insight into the simultaneous wave- and wind-structure response of floating offshore wind components can be difficult to generate. Given the aforementioned challenges in classic hydrodynamic experiments, the motivation of this project is to establish a real-time hybrid simulation (RTHS) approach that can apply aero- and hydro-dynamic loading by augmenting wave-only experimental facilities with virtual aerodynamic forces through numerical models representing the remaining dynamic forces. RTHS is a physical-numerical approach that partitions a prototype system into physical and numerical sub-assemblies that interact with each other through actuators and sensors in real time. In coupling physical and numerical models, the hybrid simulation approach applied herein is ideal for problems with: (1) structures subjected to different scaling laws, such as floating offshore wind turbines subjected to combined aero/hydro-dynamic loading, (2) structures that are too large or complex to be tested entirely in a laboratory setting, such as deep-water mooring applications, and (3) component testing, where the behavior of a portion of the assembly is uncertain but still interacts with other portions of the structure, such as testing the fatigue life of turbine blades. Few U.S. experimental facilities are able to test simultaneous aero- and hydro-dynamic loading and none can accurately produce aero/hydro-dynamic response on scaled FOWT models due to conflicting similitude laws between the wind (commonly Reynolds) and the waves (commonly Froude). To aid in accelerating the development of the U.S. floating offshore industry, there is a significant need to develop a flexible, modular framework that can expand the capacities of existing wave-only laboratories. The project goal is to demonstrate a hydrodynamic real-time hybrid simulation (hydro-RTHS) framework that couples numerical wind and physical waves acting on a FOWT, thus representing simultaneous aero/hydro-dynamic loading. The FOWT is partitioned into a full-scale numerical sub-assembly associated with the aerodynamics and a model-scale physical sub-assembly associated with the hydrodynamics. The numerical-physical partition associated with hydro-RTHS mitigates scaling constraints by supplying different scaling laws to the physical and numerical sub-assemblies. Herein, length, force, and time are scaled and exchanged between the sub-assemblies using Froude scaling to represent the open-channel flow in the physical sub-assembly. Other similitude laws could also be utilized depending on the problem definition. It is envisioned that the ability to model FOWTs under waves and wind, with mitigation of similitude distortions, would result in reduced development costs (currently, FOWT concept development is performed with full-size pro- totypes at enormous expense and risk) and increase the reliability of the FOWT industry (since extreme wave and wind conditions and contingency events can be tested safely in a controlled environment).

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Nonequilibrium effects and transverse spherocity in ultrarelativistic proton-nucleus collisions

We investigate the effects of nonequilibrium dynamics in small colliding systems by comparing a nonequilibrium transport approach, Parton-Hadron-String Dynamics (PHSD), with a (2+1)-dimensional viscous hydrodynamic model, VISHNew. Focusing on p + Pb collisions at energies available at the Large Hadron Collider, we extract the initial conditions for the hydrodynamic model from PHSD, in order to reduce the impact of the early out-of-equilibrium dynamics and focus on the traces of nonequilibrium in the ensuing medium evolution. We find that in the transport approach quantities like energy density and bulk viscous pressure are highly inhomogeneous on the transverse plane during the whole evolution, whereas the hydrodynamic simulations dissolve more efficiently the initial spatial irregularities, still keeping a high degree of inhomogeneity due to the smaller size and lifetime of the medium produced in p + Pb collisions with respect to heavy-ion reactions. As a first step that will help to identify the impact of these nonequilibrium effects on final observables in proton-nucleus collisions, we perform an analysis of the transverse spherocity, an event-shape observable able to distinguish between jetty and isotropic configurations of transverse momenta. We found that the spherocity distribution in PHSD is slightly shifted towards the isotropic limit with respect to the hydrodynamic result. Even though this dissimilarity is partially due to the difference in the final charged particle production, it mainly comes from the different description within the two frameworks of the medium produced in small colliding systems. This finding supports the idea that multidifferential measurements, such as those based on event categorization according to multiplicity and spherocity, are useful to study final-state observables in ultrarelativistic proton-nucleus collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Exploiting 20 Ne Isotopes for Precision Characterizations of Collectivity in Small Systems

Whether or not femto-scale droplets of quark-gluon plasma (QGP) are formed in so-called small systems at high-energy colliders is a pressing question in the phenomenology of the strong interaction. For proton-proton or proton-nucleus collisions the answer is inconclusive due to the large theoretical uncertainties plaguing the description of these processes. While upcoming data on collisions of 16 O nuclei may mitigate these uncertainties in the near future, here we demonstrate the unique possibilities offered by complementing 16 O + 16 O data with collisions of 20 Ne ions. We couple both nuclear lattice effective field theory (NLEFT) and projected generator coordinate method (PGCM) ab initio descriptions of the structure of 20 Ne and 16 O to hydrodynamic simulations of 16 O + 16 O and 20 Ne + 20 Ne collisions at high energy. We isolate the imprints of the bowling-pin shape of 20 Ne on the collective flow of hadrons, which can be used to perform quantitative tests of the hydrodynamic QGP paradigm. In particular, we predict that the elliptic flow of 20 Ne + 20 Ne collisions is enhanced by as much as 1.174⁢(8) stat ⁢(31) syst for NLEFT and 1.139⁢(6) stat ⁢(39) syst for PGCM relative to 16 O + 16 O collisions for the 1% most central events. At the same time, theoretical uncertainties largely cancel when studying relative variations of observables between two systems. This demonstrates a method based on experiments with two light-ion species for precision characterizations of the collective dynamics and its emergence in a small system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Striving to translate shale physics across ten orders of magnitude: What have we learned?

Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO 2 , or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.

58 GEOSCIENCES↗
DOE OSTI2025

Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr 0.78 Y 0.22 O 3-δ

Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr 0.78 Y 0.22 O 3-δ using machine learning-accelerated ab initio molecular dynamics simulations. Here, our results reproduce the experimentally observed non-monotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration—two mechanisms that influence the lattice expansion in opposite directions. The importance of this work lies in its detailed demonstration of how advanced computational techniques can accurately capture complex environmental effects, providing a valuable framework for modeling similar phenomena in a variety of material systems and applications.

Proton conducting fuel cell↗
DOE OSTI2025

Formation of Larger Solvation Shells in a LiFSI Salt Solution for Enhanced Li + Transport

Electrodes and electrolytes in Li-ion batteries (LIBs) are crucial for their electrochemical performance, and fabricating these materials to achieve the desired properties for improved efficiency is challenging. A recent study published in Nature ( Nature 2024, 627, 101−107) outlined guidelines for designing an electrolyte using fluoroacetonitrile (FACN), a small-molecule solvent with low solvation energy. This characteristic enables the formation of an anion-rich inorganic interphase in a 1.3 M lithium bis(fluorosulfonyl)imide (LiFSI) solution, facilitating higher ionic conductivity, even at low temperatures. The formation of a ligand channel was postulated and demonstrated in the simulation due to the attraction of small solvent molecules from the secondary solvation shell to the Li + ion in the primary solvation shell, thereby enhancing Li + transport. While that study primarily focused on Li + ion behavior in LIBs, the behavior of FACN in solutions with different lithium salts and what makes LiFSI in FACN outperform other systems remain to be explored. In conclusion, using an experimental technique sensitive to FACN molecules, here we directly observe formation of larger solvation shells in a LiFSI solution compared to the Li salts with different anions, consistent with the postulated ligand-channel-mechanism and potentially paving the way to achieving enhanced ionic conductivity and energy density during LIB operation.

Batteries↗
DOE OSTI2024

Cavity Controlled Upconversion in CdSe Nanoplatelet Polaritons

Exciton-polaritons provide a versatile platform for investigating quantum electrodynamics effects in chemical systems, such as polariton-altered chemical reactivity. However, using polaritons in chemical contexts will require a better understanding of their photophysical properties under ambient conditions, where chemistry is typically performed. Here, we used cavity quality factor to control strong light–matter interactions and in particular the excited state dynamics of colloidal CdSe nanoplatelets (NPLs) coupled to a Fabry– Pérot optical cavity. With increasing cavity quality factor, we observe significant population of the upper polariton (UP) state, exemplified by the rare observation of substantial UP photoluminescence (PL). Excitation of the lower polariton (LP) states results in upconverted PL emission from the UP branch due to efficient exchange of population between the LP, UP and the reservoir of dark states present in collectively coupled polaritonic systems. In addition, we measure time scales for polariton dynamics ~100 ps, implying great potential for NPL based polariton systems to affect photochemical reaction rates. State-of-the-art quantum dynamical simulations show outstanding quantitative agreement with experiments, and thus provide important insight into polariton photophysical dynamics of collectively coupled nanocrystal-based systems. These findings represent a significant step toward the development of practical polariton photochemistry platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Magnetic charge propagation upon a 3D artificial spin-ice

Magnetic charge propagation in spin-ice materials has yielded a paradigm-shift in science, allowing the symmetry between electricity and magnetism to be studied. Recent work is now suggesting the spin-ice surface may be important in mediating the ordering and associated phase space in such materials. Here, we detail a 3D artificial spin-ice, which captures the exact geometry of bulk systems, allowing magnetic charge dynamics to be directly visualized upon the surface. Using magnetic force microscopy, we observe vastly different magnetic charge dynamics along two principal directions. For a field applied along the surface termination, local energetics force magnetic charges to nucleate over a larger characteristic distance, reducing their magnetic Coulomb interaction and producing uncorrelated monopoles. In contrast, applying a field transverse to the surface termination yields highly correlated monopole-antimonopole pairs. Detailed simulations suggest it is the difference in effective chemical potential as well as the energy landscape experienced during dynamics that yields the striking differences in monopole transport.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Phase behaviours of superionic water at planetary conditions

Most water in the Universe may be superionic, and its thermodynamic and transport properties are crucial for planetary science but difficult to probe experimentally or theoretically. We use machine learning and free-energy methods to overcome the limitations of quantum mechanical simulations and characterize hydrogen diffusion, superionic transitions and phase behaviours of water at extreme conditions. Furthermore, we predict that close-packed superionic phases, which have a fraction of mixed stacking for finite systems, are stable over a wide temperature and pressure range, whereas a body-centred cubic superionic phase is only thermodynamically stable in a small window but is kinetically favoured. Our phase boundaries, which are consistent with existing—albeit scarce—experimental observations, help resolve the fractions of insulating ice, different superionic phases and liquid water inside ice giants.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electron beam-induced nanopores in Bernal-stacked hexagonal boron nitride

Controlling the size and shape of nanopores in two-dimensional materials is a key challenge in applications such as DNA sequencing, sieving, and quantum emission in artificial atoms. Here, we investigate experimentally and theoretically triangular vacancies in (unconventional) Bernal-stacked AB-h-BN formed using a high-energy electron beam. Due to the geometric configuration of AB-h-BN, triangular pores in different layers are aligned, and their sizes are controlled by the duration of the electron irradiation. Interlayer covalent bonding at the vacancy edge is not favored, as opposed to what occurs in the more common AA'-stacked BN. A variety of monolayer, concentric and bilayer pores in bilayer AB-h-BN are observed in high-resolution transmission electron microscopy and characterized using ab initio simulations. Bilayer pores in AB-h-BN are commonly formed, and grow without breaking the bilayer character. Nanopores in AB-h-BN exhibit a wide range of electronic properties, ranging from half-metallic to non-magnetic and magnetic semiconducting. Therefore, because of the controllability of the pore size, the electronic structure is also highly controllable in these systems, and can potentially be tuned for particular applications.

2D materials↗
DOE OSTI2024

Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning

Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order–disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale.

Chemistry↗
DOE OSTI2023

High-energy synchrotron flares powered by strongly radiative relativistic magnetic reconnection: 2D and 3D PIC simulations

ABSTRACT The time evolution of high-energy synchrotron radiation generated in a relativistic pair plasma energized by reconnection of strong magnetic fields is investigated with 2D and 3D particle-in-cell (PIC) simulations. The simulations in this 2D/3D comparison study are conducted with the radiative PIC code OSIRIS, which self-consistently accounts for the synchrotron radiation reaction on the emitting particles, and enables us to explore the effects of synchrotron cooling. Magnetic reconnection causes compression of the plasma and magnetic field deep inside magnetic islands (plasmoids), leading to an enhancement of the flaring emission, which may help explain some astrophysical gamma-ray flare observations. Although radiative cooling weakens the emission from plasmoid cores, it facilitates additional compression there, further amplifying the magnetic field B and plasma density n, and thus partially mitigating this effect. Novel simulation diagnostics utilizing 2D histograms in the n-B space are developed and used to visualize and quantify the effects of compression. The n-B histograms are observed to be bounded by relatively sharp power-law boundaries marking clear limits on compression. Theoretical explanations for some of these compression limits are developed, rooted in radiative resistivity or 3D kinking instabilities. Systematic parameter-space studies with respect to guide magnetic field, system size, and upstream magnetization are conducted and suggest that stronger compression, brighter high-energy radiation, and perhaps significant quantum electrodynamic effects such as pair production, may occur in environments with larger reconnection-region sizes and higher magnetization, particularly when magnetic field strengths approach the critical (Schwinger) field, as found in magnetar magnetospheres.

Schoeffler, K. M. (ORCID:0000000276447381)↗
DOE OSTI2025

SAGAbg. III. Environmental Stellar Mass Functions, Self-quenching, and the Stellar-to-halo Mass Relation in the Dwarf Galaxy Regime

Recent efforts have extended our view of the number and properties of satellite galaxies beyond the Local Group firmly down to M ⋆ ∼ 10 6 M ⊙ . A similarly complete view of the field dwarf population has lagged behind. Using the background galaxy sample from the Satellites Around Galactic Analogs (SAGA) survey at z < 0.05, we take inventory of the dwarf population down to M ⋆ ∼ 5 × 10 6 M ⊙ using three metrics: the stellar mass function (SMF) as a function of environment, the stellar-to-halo mass relation (SHMR) of dwarf galaxies inferred via abundance matching, and the quenched fraction of highly isolated dwarfs. We find that the low-mass SMF shape shows minimal environmental dependence, with the field dwarf SMF described by a low-mass power-law index of α 1 = −1.44 ± 0.09 down to M ⋆ ∼ 5 × 10 6 M ⊙ , and that the quenched fraction of isolated dwarfs drops monotonically to f q ∼ 10 −3 at M ⋆ ∼ 10 8.5 M ⊙ . Though slightly steeper than estimates from H I kinematic measures, our inferred SHMR agrees with literature measurements of satellite systems, consistent with minimal environmental dependence of the SHMR in the probed mass range. Finally, although most contemporary cosmological simulations against which we compare accurately predict the SAGAbg-SMF SHMR, we find that big-box cosmological simulations largely overpredict isolated galaxy quenched fractions via a turnaround in f q (M ⋆ ) at 10 8 ≲ M ⋆ /M ⊙ ≲ 10 9 , underscoring the complexities in disentangling the drivers of galaxy formation and the need for systematic multidimensional observations of the dwarf population across environments.

Kado-Fong, Erin [Yale Univ., New Haven, CT (United↗
DOE OSTI2022

Semi-automated Design of Artificial Intelligence Earth Science Models

Prediction and observation of water cycles involve not only patterns isolated in space and time, but rather modeling complex spatio-temporal relationships across multiple sources of data and domains. For instance, Evapotranspiration (ET) and Leaf Area Indexes (LAI) are two critical components in DOE’s Energy Exascale Earth System Model (E3SM). Accurate assessments of ET and LAI are critical for understanding hydrological processes, deforestation, crop yield, and irrigation impacts. However, current ET estimates for global simulations are available at very coarse spatial resolution. They are usually derived from satellite data based on broad plant functional types (PFT), which fail to capture the fine-scale variations due to change in vegetation type across the globe. Within this context and in light of the data-model integration challenges highlighted in the EESSD Strategic Plan, the new era of AI model development for geosciences calls for data-driven methods that provide domain scientists with estimations of parameters such as PFT and LAI in an efficient, interpretable, and easy-to-operate manner.

Ambrozio Dias, Philipe↗
DOE OSTI2023

Quantum simulation of an exotic quantum critical point in a two-site charge Kondo circuit

Tuning a material to the cusp between two distinct ground states can produce physical properties that are unlike those in either of the neighbouring phases. Advances in the fabrication and control of quantum systems have raised the tantalizing prospect of artificial quantum simulators that can capture such behaviour. A tunable array of coupled qubits should have an appropriately rich phase diagram, but realizing such a system with either tunnel-coupled semiconductor quantum dots or metal nanostructures has proven difficult. The challenge for scaling up to clusters or lattices is to ensure that each element behaves essentially identically and that the coupling between elements is uniform, while also maintaining tunability of the interactions. Here we study a nanoelectronic circuit comprising two coupled hybrid metal–semiconductor islands, combining the strengths of both materials to form a potentially scalable platform. The semiconductor component allows for controllable inter-site couplings at quantum point contacts, while the metal component’s effective continuum of states means that different sites can be made equivalent by tuning local potentials. The couplings afforded by this architecture can realize an unusual quantum critical point resulting from frustrated Kondo interactions. In this study, the observed critical behaviour matches theoretical predictions, verifying the success of our experimental quantum simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape: Modeling Archive

This modeling archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025). This archive contains model input files and outputs from landscape-scale simulations conducted using ELM, the land model component of the Department of Energy’s Energy Exascale Earth System Model (E3SM), at the Council NGEE Arctic field site (Council Road mile marker 71) on Alaska’s Seward Peninsula. Input data and model output from two sets of ELM simulations are provided. The first set of simulations were conducted with the two default ELM Arctic plant functional types (PFTs; broadleaf deciduous boreal shrub and a C3 grass) and the second set of simulations were conducted with a set of nine Arctic-specific PFTs including nonvascular mosses and lichens, graminoids, forbs, evergreen dwarf shrubs, three height classes of deciduous shrubs (dwarf, low, and low to tall), and deciduous alder shrubs (Sulman et al., 2021). Parameter names and major parameter changes in the Arctic-specific PFT configuration are described in Sulman et al. (2021) and archived in the Sulman et al. (2021) dataset (see below). Simulations were spatially explicit, covering an approximately 6.4X3.3 km domain at the Council site with a spatial resolution of 100 m for a total of 2,112 simulated grid cells under each ELM PFT configuration. The modeling archive contains meteorological forcing (seven *.nc files and one *.txt file), a domain definition file (one *.nc files), land surface configuration files (two *.nc files), parameter files (two *.nc files), annual ELM output files spanning 1980-2014 (68 *.nc files), and a User’s Guide (*pdf file). Additional information on the provided files is in the “Modeling Archive Contents” section of the User’s Guide. Model outputs are aggregated to the column scale (i.e. PFT-specific outputs are not provided here).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗
DOE OSTI2024

Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification

Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗