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DOE OSTI2021

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Abstract Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li 7 P 3 S 11 , a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

Chemistry↗

DOE OSTI2022

Efficient hybrid explicit-implicit learning for multiscale problems

Splitting method is a powerful method to handle application problems by splitting physics, scales, domain, and so on. Many splitting algorithms have been designed for efficient temporal discretization. Here, in this paper, our goal is to use temporal splitting concepts in designing machine learning algorithms and, at the same time, help splitting algorithms by incorporating data and speeding them up. We propose a machine learning assisted splitting scheme which improves the efficiency of the scheme meanwhile preserves the accuracy. We consider a recently introduced multiscale splitting algorithms, where the multiscale problem is solved on a coarse grid. To approximate the dynamics, only a few degrees of freedom are solved implicitly, while others explicitly. This splitting concept allows identifying degrees of freedom that need implicit treatment. In this paper, we use this splitting concept in machine learning and propose several strategies. First, the implicit part of the solution can be learned as it is more difficult to solve, while the explicit part can be computed. This provides a speed-up and data incorporation for splitting approaches. Secondly, one can design a hybrid neural network architecture because handling explicit parts requires much fewer communications among neurons and can be done efficiently. Thirdly, one can solve the coarse grid component via PDEs or other approximation methods and construct simpler neural networks for the explicit part of the solutions. We discuss these options and implement one of them by interpreting it as a machine translation task. This interpretation of the splitting scheme successfully enables us using the Transformer since it can perform model reduction for multiple time series and learn the connection between them. We also find that the splitting scheme is a great platform to predict the coarse solution with insufficient information of the target model: the target problem is partially given and we need to solve it through a known problem which approximates the target. Our machine learning model can incorporate and encode the given information from two different problems and then solve the target problems. We conduct four numerical examples and the results show that our method is stable and accurate.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Voltage waveform tailoring for high aspect ratio plasma etching of SiO 2 using Ar/CF 4 /O 2 mixtures: Consequences of low fundamental frequency biases

The use of non-sinusoidal waveforms in low pressure capacitively coupled plasmas intended for microelectronics fabrication has the goal of customizing ion and electron energy and angular distributions to the wafer. One such non-sinusoidal waveform uses the sum of consecutive harmonics of a fundamental sinusoidal frequency, f 0 , having a variable phase offset between the fundamental and even harmonics. In this paper, we discuss results from a computational investigation of the relation between ion energy and DC self-bias when varying the fundamental frequency f 0 for capacitively coupled plasmas sustained in Ar/CF 4 /O 2 and how those trends translate to a high aspect ratio etching of trenches in SiO 2 . The fundamental frequency, f 0 , was varied from 1 to 10 MHz and the relative phase from 0° to 180°. Two distinct regimes were identified. Average ion energy onto the wafer is strongly correlated with the DC self-bias at high f 0 , with there being a maximum at φ = 0° and minimum at φ = 180°. In the low frequency regime, this correlation is weak. Average ion energy onto the wafer is instead dominated by dynamic transients in the applied voltage waveforms, with a maximum at φ = 180° and minimum at φ = 0°. The trends in ion energy translate to etch properties. In both, the high and low frequency regimes, higher ion energies translate to higher etch rates and generally preferable final features, though behaving differently with phase angle.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Actuating Liquid Crystals Rapidly and Reversibly by Using Chemical Catalysis

Abstract Microtubules and catalytic motor proteins underlie the microscale actuation of living materials, and they have been used in reconstituted systems to harness chemical energy to drive new states of organization of soft matter (e.g., liquid crystals (LCs)). Such materials, however, are fragile and challenging to translate to technological contexts. Rapid (sub‐second) and reversible changes in the orientations of LCs at room temperature using reactions between gaseous hydrogen and oxygen that are catalyzed by Pd/Au surfaces are reported. Surface chemical analysis and computational chemistry studies confirm that dissociative adsorption of H 2 on the Pd/Au films reduces preadsorbed O and generates 1 ML of adsorbed H, driving nitrile‐containing LCs from a perpendicular to a planar orientation. Subsequent exposure to O 2 leads to oxidation of the adsorbed H, reformation of adsorbed O on the Pd/Au surface, and a return of the LC to its initial orientation. The roles of surface composition and reaction kinetics in determining the LC dynamics are described along with a proof‐of‐concept demonstration of microactuation of beads. These results provide fresh ideas for utilizing chemical energy and catalysis to reversibly actuate functional LCs on the microscale.

Chemistry↗

DOE OSTI2021

Local rules for fabricating allosteric networks

Mechanical properties of disordered networks can be significantly tailored by modifying a small fraction of their bonds. This procedure has been used to design and build mechanical metamaterials with a variety of responses. A long-range “allosteric” response, where a localized input strain at one site gives rise to a localized output strain at a distant site, has been of particular interest. This work presents an approach to incorporating allosteric responses in experimental systems by pruning disordered networks in situ. Previous work has relied on computer simulations to design and predict the response of such systems using a cost function where the response of the entire network to each bond removal is used at each step to determine which bond to prune. It is not feasible to follow such a design protocol in experiments where one has access only to local response at each site. This paper presents design algorithms that allow determination of what bonds to prune based purely on the local forces in the network without employing a cost function; using only local information, allosteric networks are designed in simulations and then built out of real materials. The results show that some pruning strategies work better than others when translated into an experimental system. A method is presented to measure local stresses experimentally in disordered networks. This approach is then used to implement pruning methods to design desired responses in situ. Results from these experiments confirm that the pruning methods are robust and work in a real laboratory material.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Spray Pyrolysis-Aerosol Deposition for the Production of Thick Yttria-Stabilized Zirconia Coatings

Aerosol deposition (AD) is a coating technique wherein particles are impacted onto a target substrate at reduced pressures, and supersonic particle impact velocities lead to coating consolidation. The limiting step in AD application is often not supersonic deposition operation, but aerosolization of powder particles with the proper size distribution; the translational impact velocity is strongly size-dependent. It is demonstrated that by directly synthesizing particles in the gas phase, size-controlled ceramic particles can be injected into AD systems. This in situ formation step obviates the need for particle aerosolization. Ultrasonic spray pyrolysis (USP) is applied to produce yttria-stabilized zirconia (YSZ), and USP is directly coupled with AD to produce consolidated, thick, YSZ coatings on metal substrates. USP-AD yields YSZ coatings on stainless steel and aluminum substrates with porosities <0.20, which grow to thicknesses beyond 100 μm. Aerodynamic particle spectrometry and electron microscopy reveal that the depositing particles are 200 nm–1.2 μm in diameter, though each particle is composed of nanocrystalline YSZ. Supporting computational fluid dynamics calculations demonstrate that the YSZ particle impact speeds are above 300 m s -1 . Thermal conductivity measurements demonstrate that USP-AD coatings have conductivities consistent with those produced from high-temperature processes.

36 MATERIALS SCIENCE↗

NASA NTRS2019

Plasma Science in Planetary Entry

Spacecraft entering a planetary atmosphere dissipate a great deal of energy into the surrounding gas. In the frame of reference of the vehicle, the atmospheric gas suddenly decelerates from hypersonic (Mach ~5-50) to subsonic velocities. The kinetic energy of the gas is rapidly converted to thermal and chemical energy, forming a bow shock behind which a plasma with energies on the order of one electron volt (eV) is produced. The resulting shock layer relaxes from strong thermal non-equilibrium that is translationally hot but internally cold and un-ionized toward a thermochemically equilibrated plasma over a distance of a few centimeters. Composition is dependent upon the planetary atmosphere – Air for Earth, CO2/N2 for Mars and Venus, N2/CH4 for Titan and H2/He/CH4 for Saturn, Neptune and Jupiter. Typical velocities of entry may range from 3-7 km/s (4-25 MJ/kg) for Titan/Mars, 8-14 km/s (30-100 MJ/kg) for Earth/Venus, and 25-40 km/s (300-800 MJ/kg) for outer planets. The equilibrium plasmas produced from these conditions are highly dissociated (up to and above 99%) and ionized (0.1- 15%), with temperatures from 7,000-15,000K and pressures from 0.1-1.0 bar. Understanding the behavior of these plasmas – the way in which they approach equilibrium, how they radiate, and how they interact with materials – is an active area of research necessitated by requirements to predict and test the performance of thermal protection systems (TPS) that enable spacecraft to deliver scientific instruments, and people, to foreign worlds and back to Earth. The endeavor is a multi-physics problem, with key processes highlighted in Fig. 1. This white paper describes the current state of the art in simulating shock layer plasmas both computationally and in ground test facilities. Gaps requiring further research and development are identified.

Cruden, Brett A.↗

DOE OSTI2026

Er Al :Al 2 ⁢O 3 for telecom-band photonics: Electronic structure and optical properties

Er-doped Al 2 ⁢O 3 is a promising host for telecom-band integrated photonics. Here, in this study, we combine ab initio calculations with a symmetry-resolved analysis to elucidate substitutional Er on the Al site (Er Al ) in 𝛼−Al 2 ⁢O 3 . First-principles relaxations confirm the structural stability of Er Al . We then use the local trigonal crystal-field symmetry to classify the Er-derived impurity levels by irreducible representations and to derive polarization-resolved electric-dipole selection rules, explicitly identifying the symmetry-allowed 𝑓−𝑑 hybridization channels. Kubo-Greenwood absorption spectra computed from Kohn-Sham states quantitatively corroborate these symmetry predictions. Furthermore, we connect the calculated intra-4⁢𝑓 line strengths to Judd-Ofelt theory, clarifying the role of 4⁢𝑓−5⁢𝑑 admixture in enabling optical activity. Notably, we predict a characteristic absorption near 1.47 µ⁢m (telecom band), relevant for on-chip amplification and emission. To our knowledge, a symmetry-resolved first-principles treatment of Er:Al 2 ⁢O 3 with an explicit Judd-Ofelt interpretation has not been reported, providing a transferable framework for tailoring rare-earth dopants in wide-band-gap oxides for integrated photonics. Our results for the optical spectra are in good agreement with experimental data. The resulting symmetry-based selection rules translate directly to polarization-dependent coupling in Al 2 ⁢O 3 integrated photonic waveguides and resonators, enabling device-level design of TE/TM-mode interaction with Er emitters in the 1.5-µ⁢m telecom band.

Er-doped Al2O3↗

DOE OSTI2023

Erasure conversion in a high-fidelity Rydberg quantum simulator

Minimizing and understanding errors is critical for quantum science, both in noisy intermediate scale quantum (NISQ) devices and for the quest towards fault-tolerant quantum computation. Rydberg arrays have emerged as a prominent platform in this context with impressive system sizes and proposals suggesting how error-correction thresholds could be significantly improved by detecting leakage errors with single-atom resolution, a form of erasure error conversion. However, two-qubit entanglement fidelities in Rydberg atom arrays have lagged behind competitors and this type of erasure conversion is yet to be realized for matter-based qubits in general. Here we demonstrate both erasure conversion and high-fidelity Bell state generation using a Rydberg quantum simulator. When excising data with erasure errors observed via fast imaging of alkaline-earth atoms, we achieve a Bell state fidelity of $\ge 0.997{1}_{-13}^{+10}$ , which improves to $\ge 0.998{5}_{-12}^{+7}$ when correcting for remaining state-preparation errors. We further apply erasure conversion in a quantum simulation experiment for quasi-adiabatic preparation of long-range order across a quantum phase transition, and reveal the otherwise hidden impact of these errors on the simulation outcome. Our work demonstrates the capability for Rydberg-based entanglement to reach fidelities in the 0.999 regime, with higher fidelities a question of technical improvements, and shows how erasure conversion can be utilized in NISQ devices. These techniques could be translated directly to quantum-error-correction codes with the addition of long-lived qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Principles of the Battery Data Genome

Batteries are central to modern society. They are no longer just a convenience but a critical enabler of the transition to a resilient, low-carbon economy. Battery development capabilities are provided by communities spanning materials discovery, battery chemistry and electrochemistry, cell and pack design, scale-up, manufacturing, and deployments. Despite their relative maturity, data-science practices among these diverse groups are far behind the state of the art in other fields, which have demonstrated an ability to significantly improve innovation and economic impact. The negative consequences of the present paradigm include incremental improvements but few breakthroughs, significant manufacturing uncertainties, and cascading investment risks that collectively slow deployments. The primary roadblock to a battery-data-science renaissance is the requirement for large amounts of high-quality data, which are not available in the current fragmented ecosystem. Here, in this study, we identify gaps and propose principles that enable the solution by building a robust community of data hubs with standardized practices and flexible sharing options that will seed advanced tools spanning innovation to deployment. Precedents are offered that demonstrate that both public good and immense economic gains will arise from sharing valuable battery data. The proposed Battery Data Genome looks to broadly transform innovations and revolutionize their translation from research to societal impact.

25 ENERGY STORAGE↗

DOE OSTI2021

Formation of a single quasicrystal upon collision of multiple grains

Abstract Quasicrystals exhibit long-range order but lack translational symmetry. When grown as single crystals, they possess distinctive and unusual properties owing to the absence of grain boundaries. Unfortunately, conventional methods such as bulk crystal growth or thin film deposition only allow us to synthesize either polycrystalline quasicrystals or quasicrystals that are at most a few centimeters in size. Here, we reveal through real-time and 3D imaging the formation of a single decagonal quasicrystal arising from a hard collision between multiple growing quasicrystals in an Al-Co-Ni liquid. Through corresponding molecular dynamics simulations, we examine the underlying kinetics of quasicrystal coalescence and investigate the effects of initial misorientation between the growing quasicrystalline grains on the formation of grain boundaries. At small misorientation, coalescence occurs following rigid rotation that is facilitated by phasons. Our joint experimental-computational discovery paves the way toward fabrication of single, large-scale quasicrystals for novel applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Willingness to Receive mHealth Services Among Patients with Diabetes on Chronic Follow-up in Public Hospitals in Eastern Ethiopia: Multicenter Mixed-Method Study

Background: Management of diabetes requires a long-term care strategy, including support for adherence to a healthy lifestyle and treatment. Exploring the willingness of patients with diabetes to receive mHealth services is essential for designing efficient and effective services. This study aimedto determine willingness to receive mHealth services and associated factors, as well as explore the barriers to receive mHealth services among patients with diabetes. Methods: A multicenter mixed-method study was employed from September 1 to November 30, 2022. For the quantitative part, a total of 365 patients with diabetes receiving chronic follow-up at three public hospitals were enrolled. Data were gathered using structured questionnaires administered by interviewers, entered into Epi-data version 4.6, and analyzed using Stata version 17. A binary and multivariable logistic regression model was computed to identify the associated factors. For qualitative, eight key informants and seven in-depth interviews were conducted. After verbatim transcription and translation, the data were thematically analyzed using ATLAS.ti V. 7.5. Results: Overall, 77.3% had access to a mobile phone, and 74.5% of them were willing to receive mHealth services. Higher odds of willingness to receive mHealth services were reported among patients with an age below 35 years [AOR = 4.11 (1.15– 14.71)], attended formal education [AOR = 2.63 (1.19– 5.77)], without comorbidity [AOR = 3.6 (1.54– 8.41)], < 1-hour travel to reach a health facility [AOR = 3.57 (1.03– 12.36)], answered unknown calls [AOR = 2.3 (1.04– 5.13)], and were satisfied with health-care provider service [AOR = 2.44 (1.04– 5.72)]. In the qualitative part, infrastructure, health facilities, socioeconomic factors, and patients’ behavioral factors were major identified barriers to receiving mHealth services. Conclusion: In this study, the willingness to receive mHealth services for those who have access to mobile phones increased. Additionally, the study highlighted common barriers to receiving mHealth services.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Proxy-based Bayesian inversion of strain tensor data measured during well tests

Recent instrument developments have made it possible to measure the strain tensor caused by injecting or pumping fluid from aquifers or reservoirs, but the full value of these data is limited because the long runtimes of poroelastic forward models makes it impractical to use many inversion schemes. This limits the interpretation of strain data for managing the recovery of resources or storage of wastes in the subsurface. This paper describes a method of inverting deformation data using a poroelastic numerical simulator so the results can be used to manage reservoirs or aquifers. We developed a workflow designed to reduce the number of simulations sufficiently to make it feasible to use DREAMzs, an advanced Bayesian inversion method that translates the uncertainties from different sources into unbiased posterior parameter distributions and uncertainty envelopes around the field data. Using a KNN proxy model for the poroelastic simulator is key to reducing the overall computations, and the workflow includes a strategy for ensuring the proxy model results converge on the results from the simulator. The workflow is tested using an idealized example that verifies the ability to correctly identify parameters and characterize noise used to perturb the data. Field data from an injection test at an oil reservoir near Tulsa, Oklahoma, are also used to evaluate the efficacy of the workflow with a real dataset. The workflow identified 265 history matching solutions out of 1240 total simulation runs (21% acceptance ratio), where the results were used to characterize posterior parameter distribution and evaluate the prediction uncertainty. Furthermore, this workflow is significant because it enables strain tensor, or other geomechanical measurements to be interpreted to guide decision-making during energy and environmental processes in the subsurface.

42 ENGINEERING↗

DOE OSTI2023

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Abstract Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm 3 IC (≃6 × 10 8 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

The Human Phenotype Ontology in 2021

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail. We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Casein kinase 1 dynamics underlie substrate selectivity and the PER2 circadian phosphoswitch

Post-translational control of PERIOD stability by Casein Kinase 1δ and ε (CK1) plays a key regulatory role in metazoan circadian rhythms. Despite the deep evolutionary conservation of CK1 in eukaryotes, little is known about its regulation and the factors that influence substrate selectivity on functionally antagonistic sites in PERIOD that directly control circadian period. Here we describe a molecular switch involving a highly conserved anion binding site in CK1. This switch controls conformation of the kinase activation loop and determines which sites on mammalian PER2 are preferentially phosphorylated, thereby directly regulating PER2 stability. Integrated experimental and computational studies shed light on the allosteric linkage between two anion binding sites that dynamically regulate kinase activity. We show that period-altering kinase mutations from humans to Drosophila differentially modulate this activation loop switch to elicit predictable changes in PER2 stability, providing a foundation to understand and further manipulate CK1 regulation of circadian rhythms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Monte Carlo Simulation with CAD Interface for Calculation of 3D Maps of Residual Dose (CRADA)

Objective: To develop an easy-to-use software application to predict and mitigate radiation effects in research environment, space instruments, nuclear plants and medical facilities and help nonproliferation and national security efforts. Tech-X will develop standalone software libraries and command-line tools for ( 1) translating CAD into tessellated surfaces and tetrahedral meshes in GDML (for Geant4 and MARS 15), ROOT (for MARS 15) and HDF5 (for compact representation and for the visualization) formats, (2) healing CAD geometries to make them suitable for Monte Carlo simulations; (3) creating uniform and variable Cartesian and cylindrical meshes for detailed scoring; and ( 4) efficient Monte Carlo navigation in CAD geometries. JLAB will finish automation of simulations of residual dose in CAD geometries and integrate Tech-X software into Geant4 and MARS15. Finally, Tech-X will develop a Graphical User Interface to set up and heal CAD geometries, create input files, run and visualize simulations for residual dose. This application will run on local desktops, local and remote clusters and supercomputers and will be made available through public clouds, such as Amazon Web Services.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2026

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗