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DOE OSTI2025

Electronic Spin Relaxation and Clustering in High-Pressure High-Temperature Synthesized Microcrystalline Diamond Particles with Reduced Nitrogen Content

The negatively charged nitrogen-vacancy (NV – ) color center in diamonds is widely studied because of numerous applications of this unique quantum system in sensing and quantum information sciences. While substitutional nitrogen is required to form the NV – centers in diamond, it also yields other paramagnetic defects─primarily the neutrally charged substitutional nitrogen centers (P1)─that decrease NV – spin coherence, which in turn degrades performance in applications. Herein, we investigate high-pressure high-temperature synthesized diamond microparticles (ca. 140–185 μm) having lower─ranging from 3 to 38 ppm─than the typical nitrogen content of type 1b diamond ( ca. 100 ppm and higher) typically used for the production of fluorescent diamond particles with NV – centers. A suite of electron paramagnetic resonance, optically detected magnetic resonance, and nuclear magnetic resonance methods are used to characterize spin properties of P1 and NV – centers in the particles. Upon decreasing the nitrogen content from 29 to 3 ppm, the ensemble NV – T 2 relaxation time increased by about 3-fold as measured directly in the Hahn Echo experiment at magnetic field of 1.2 T. Analysis of electronic relaxation of P1 centers revealed the existence of at least two distinct populations of P1 centers, consisting of fast and slower relaxing spins and allowed for an estimation of local concentrations. Even with <10 ppm nitrogen contents, the analysis indicated a highly heterogeneous distribution of P1 centers, suggesting the possibility of P1 spin clustering even at low nitrogen concentrations. The combined data demonstrate that the particles prepared from HPHT diamond with a low nitrogen content offer improved spin properties that are beneficial for NV – sensing applications.

Carbon↗

DOE OSTI2022

Transit Time Estimation in Catchments: Recent Developments and Future Directions

Abstract Water transit time is now a standard measure in catchment hydrological and ecohydrological research. The last comprehensive review of transit time modeling approaches was published 15+ years ago. But since then the field has largely expanded with new data, theory and applications. Here, we review these new developments with focus on water‐age‐balance approaches and data‐based approaches. We discuss and compare methods including StorAge‐Selection functions, well/partially mixed compartments, water age tracking through spatially distributed models, direct transit time estimates from controlled experiments, young water fractions, and ensemble hydrograph separation. We unify some of the heterogeneity in the literature that has crept in with these many new approaches, in an attempt to clarify the key differences and similarities among them. Finally, we point to open questions in transit time research, including what we still need from theory, models, field work, and community practice.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Probing reaction channels via reinforcement learning

Abstract Chemical reactions are dynamical processes involving the correlated reorganization of atomic configurations, driving the conversion of an initial reactant into a result product. By virtue of the metastability of both the reactants and products, chemical reactions are rare events, proceeding fleetingly. Reaction pathways can be modelled probabilistically by using the notion of reactive density in the phase space of the molecular system. Such density is related to a function known as the committor function, which describes the likelihood of a configuration evolving to one of the nearby metastable regions. In theory, the committor function can be obtained by solving the backward Kolmogorov equation (BKE), which is a partial differential equation (PDE) defined in the full dimensional phase space. However, using traditional methods to solve this problem is not practical for high dimensional systems. In this work, we propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of states that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based PDE solver to obtain an approximation solution of a restricted BKE, even when the dimension of the problem is very high. The resulting solution provides an approximation for the committor function that encodes mechanistic information for the reaction, paving a new way for understanding of complex chemical reactions and evaluation of reaction rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Detailed analysis of excited-state systematics in a lattice QCD calculation of 𝑔 𝐴

Excited state contamination remains one of the most challenging sources of systematic uncertainty to control in lattice QCD calculations of nucleon matrix elements and form factors: early time separations are contaminated by excited states and late times suffer from an exponentially bad signal-to-noise problem. High-statistics calculations at large time separations ≳ 1 fm are commonly used to combat these issues. In this work, focusing on g A , we explore the alternative strategy of utilizing a large number of relatively low-statistics calculations at short to medium time separations (0.2–1 fm), combined with a multistate analysis. On an ensemble with a pion mass of approximately 310 MeV and a lattice spacing of approximately 0.09 fm, we find this provides a more robust and economical method of quantifying and controlling the excited state systematic uncertainty. A quantitative separation of various types of excited states enables the identification of the transition matrix elements as the dominant contamination. The excited state contamination of the Feynman-Hellmann correlation function is found to reduce to the 1% level at approximately 1 fm while, for the more standard three-point functions, this does not occur until after 2 fm. Critical to our findings is the use of a global minimization, rather than fixing the spectrum from the two-point functions and using them as input to the three-point analysis. We find that the ground state parameters determined in such a global analysis are stable against variations in the excited state model, the number of excited states, and the truncation of early-time or late-time numerical data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Integrating an Ensemble Reward System into an Off-Policy Reinforcement Learning Algorithm for the Economic Dispatch of Small Modular Reactor-Based Energy Systems

Nuclear Integrated Energy Systems (NIES) have emerged as a comprehensive solution for navigating the changing energy landscape. They combine nuclear power plants with renewable energy sources, storage systems, and smart grid technologies to optimize energy production, distribution, and consumption across sectors, improving efficiency, reliability, and sustainability while addressing challenges associated with variability. The integration of Small Modular Reactors (SMRs) in NIES offers significant benefits over traditional nuclear facilities, although transferring involves overcoming legal and operational barriers, particularly in economic dispatch. This study proposes a novel off-policy Reinforcement Learning (RL) approach with an ensemble reward system to optimize economic dispatch for nuclear-powered generation companies equipped with an SMR, demonstrating superior accuracy and efficiency when compared to conventional methods and emphasizing RL’s potential to improve NIES profitability and sustainability. Finally, the research attempts to demonstrate the viability of implementing the proposed integrated RL approach in spot energy markets to maximize profits for nuclear-driven generation companies, establishing NIES’ profitability over competitors that rely on fossil fuel-based generation units to meet baseload requirements.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1982

Theory of radio occultation by Saturn's rings

The radio occultation technique, as applied to Saturn's rings, is developed as a new method for the study of the physical properties of planetary ring systems. The rings are treated as a Doppler-spread radar target composed of an ensemble of discrete scatterers. The mathematical formulation of the received signal as a random-phasor-sum process is carried out following a conventional radar theory approach, providing a convenient starting point for deriving coherent signal parameters. A classical result is rederived for the equivalent refractive index of the medium. The analysis is generalized to include ringlets of arbitrary width and it is shown that when the width is such that two adjacent rays are differentially perturbed in phase, ray bending that causes focusing of the coherent signal may result. The diffuse component is also treated in detail.

Marouf, E. A.↗

NASA NTRS1986

Cosmic rays near the heliospheric current sheet. II - An ensemble approach to comparing theory and observation

A quantitative comparison is carried out between theoretical predictions and observations of the intensity of galactic cosmic rays near the interplanetary current sheet. Model calculations are compared with a statistical analysis of observations of galactic cosmic rays at the earth and the simultaneous position of the current sheet. Since the observations were made over a period of several years, the inclination of the current sheet varied considerably, and comparison with any one model calculation referring to a given inclination would not be appropriate. An ensemble of different current sheet inclinations are used to generate expected values with the model in order to make the analysis of the computations approximate the method used to analyze the data. Agreement is found between theory and observation at energies of the order of a few GeV.

Jokipii, J. R.↗

NASA NTRS1995

Dependence of the multiplicities of secondary particles on the impact parameter in collisions of high-energy neon and iron nuclei with photoemulsion nuclei

A method is proposed for finding the dependence of mean multiplicities of secondaries on the nucleus-collision impact parameter from the data on the total interaction ensemble. The impact parameter has been shown to completely define the mean characteristics of an individual interaction event. A difference has been found between experimental results and the data calculated in terms of the cascade-evaporation model at impact-parameter values below 3 fm.

Dudkin, V. E.↗

NASA NTRS2002

Computing Aerodynamic Performance of a 2D Iced Airfoil: Blocking Topology and Grid Generation

The ice accrued on airfoils can have enormously complicated shapes with multiple protruded horns and feathers. In this paper, several blocking topologies are proposed and evaluated on their ability to produce high-quality structured multi-block grid systems. A transition layer grid is introduced to ensure that jaggedness on the ice-surface geometry do not to propagate into the domain. This is important for grid-generation methods based on hyperbolic PDEs (Partial Differential Equations) and algebraic transfinite interpolation. A 'thick' wrap-around grid is introduced to ensure that grid lines clustered next to solid walls do not propagate as streaks of tightly packed grid lines into the interior of the domain along block boundaries. For ice shapes that are not too complicated, a method is presented for generating high-quality single-block grids. To demonstrate the usefulness of the methods developed, grids and CFD solutions were generated for two iced airfoils: the NLF0414 airfoil with and without the 623-ice shape and the B575/767 airfoil with and without the 145m-ice shape. To validate the computations, the computed lift coefficients as a function of angle of attack were compared with available experimental data. The ice shapes and the blocking topologies were prepared by NASA Glenn's SmaggIce software. The grid systems were generated by using a four-boundary method based on Hermite interpolation with controls on clustering, orthogonality next to walls, and C continuity across block boundaries. The flow was modeled by the ensemble-averaged compressible Navier-Stokes equations, closed by the shear-stress transport turbulence model in which the integration is to the wall. All solutions were generated by using the NPARC WIND code.

Chi, X.↗

NASA NTRS1993

Dependence of the multiplicities of secondary particles on the impact parameter in collisions of high-energy neon and iron nuclei with photoemulsion nuclei

Non-NASA Center↗

NASA NTRS2023

Statistical Approach on Utilizing Ground-Based Experiments to Model Break-Up Events

Two laboratory-based impact tests have been conducted to develop and extend the capability of NASA satellite breakup models. The first experiment, the Satellite Orbital Debris Characterization Impact Test (SOCIT), was conducted by the U.S. Department of Defense and NASA in 1992. It employed a fully functional U.S. Navy Transit spacecraft, fabricated from materials commonly used in the 1960s. SOCIT fragmentation data formed the basis of the current NASA Standard Satellite Breakup Model (SSBM). A second experiment, DebriSat, was conducted in 2014 by the DebriSat consortium: the NASA Orbital Debris Program Office, the United States Space Force Space Systems Command, formerly the Air Force Space and Missile Systems Center, the Air Force Arnold Engineering Development Complex, and the University of Florida. This impact test was performed on a high-fidelity mock-up satellite assembled from modern components. Data from both experiments are expected to contribute to the next-generation models for on-orbit breakup analyses, long-term environment predictions, and debris risk assessment. This paper uses the direct statistical sampling of the SOCIT and DebriSat data ensembles to model an on-orbit breakup event, rather than the analytic expressions estimated from the samples in the SSBM and its predecessor. This direct method involves drawing fragments (i.e., sampling with replacement) from each fragmentation data-subset containing mass, shape category, material density, characteristic length, mass, and cross-sectional area. As part of the sampling process, the properties of sampled fragment data (e.g., area-to-mass ratios) are numerically checked to ensure they do not contain unrealistic quantities. The process to simulate a breakup cloud composed of fragments from SOCIT and DebriSat datasets is discussed. The methodology for ensuring that conservation of overall mass of the sampled fragment cloud under this sampling approach is highlighted. Finally, the results are compared with simulated clouds generated from SSBM for specific historical breakup events.

Andrew Vavrin↗

DOE OSTI2022

Entanglement features of random neural network quantum states

Restricted Boltzmann machines (RBMs) are a class of neural networks that have been successfully employed as a variational ansatz for quantum many-body wave functions. Here, we develop an analytic method to study quantum many-body spin states encoded by random RBMs with independent and identically distributed complex Gaussian weights. By mapping the computation of ensemble-averaged quantities to statistical mechanics models, we are able to investigate the parameter space of the RBM ensemble in the thermodynamic limit. We discover qualitatively distinct wave functions by varying RBM parameters, which correspond to distinct phases in the equivalent statistical mechanics model. Notably, there is a regime in which the typical RBM states have near-maximal entanglement entropy in the thermodynamic limit, similar to that of Haar-random states. However, these states generically exhibit nonergodic behavior in the Ising basis, and do not form quantum state designs, making them distinguishable from Haar-random states.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗

DOE OSTI2020

Fluorinated Covalent Organic Frameworks: A Novel Pathway to Enhance Hydrogen Sorption and Control Isosteric Heats of Adsorption; HyMARC Seed Project Final Report

Organic framework materials, as a unique sub-class of carbon-based sorbents, have gained increasing attention for promising attributes toward gas storage. Calculations of the hydrogen capacity of optimized frameworks show potential to achieve greater than 60 g/L storage of hydrogen, placing metal-organic frameworks (MOFs) and COFs near the top of the class of porous materials. However, at the ensemble level, poor stacking creates a quasi-amorphous material with low structural integrity and low effective surface area. The chemical versatility of COFs allows for additional methods for producing long-range order that specifically target the inter-layer interactions in COFs but that leave key pore-accessible linker sites open. These modifications (either during synthesis or post-synthetic) enable a host of strategies to both improve crystalline order for more stable and higher surface, as well as add metals with open coordination for enhanced H 2 binding enthalpy.

08 HYDROGEN↗

DOE OSTI2023

Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements

Abstract. Cloud droplet number concentration (Nd) is crucial for understanding aerosol–cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based Nd retrievals based on comprehensive datasets from the Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The Nd retrieval methods use ARM ENA observatory ground-based remote sensing observations from a micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP (Droplet Number Concentration) value-added product (VAP), all of which also retrieve cloud effective radius (re). The retrievals are compared against aircraft measurements from the fast cloud droplet probe (FCDP) and the cloud and aerosol spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the Nd and cloud re retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to 4 years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that Nd (re) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify Nd retrieval uncertainties and identify reliable Nd retrieval scenarios. Of the retrieval methods, we recommend using the micropulse lidar-based method. This method has good agreement with in situ measurements, less sensitivity to issues arising from precipitation and low cloud LWP and/or optical depth, and broad applicability by functioning for both daytime and nighttime conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluation of two-particle properties within finite-temperature self-consistent one-particle Green’s function methods: Theory and application to GW and GF2

One-particle Green’s function methods can model molecular and solid spectra at zero or non-zero temperatures. One-particle Green’s functions directly provide electronic energies and one-particle properties, such as dipole moment. However, the evaluation of two-particle properties, such as $\langle$S 2 $\rangle$ and $\langle$N 2 $\rangle$, can be challenging because they require a solution of the computationally expensive Bethe–Salpeter equation to find two-particle Green’s functions. We demonstrate that the solution of the Bethe–Salpeter equation can be completely avoided. Applying the thermodynamic Hellmann–Feynman theorem to self-consistent one-particle Green’s function methods, we derive expressions for two-particle density matrices in a general case and provide explicit expressions for GF2 and GW methods. Such density matrices can be decomposed into an antisymmetrized product of correlated one-electron density matrices and the two-particle electronic cumulant of the density matrix. Cumulant expressions reveal a deviation from ensemble representability for GW, explaining its known deficiencies. We analyze the temperature dependence of $\langle$S 2 $\rangle$ and $\langle$N 2 $\rangle$ for a set of small closed-shell systems. Interestingly, both GF2 and GW show a non-zero spin contamination and a non-zero fluctuation of the number of particles for closed-shell systems at the zero-temperature limit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Exploring cryo-electron microscopy with molecular dynamics

Single particle analysis cryo-electron microscopy (EM) and molecular dynamics (MD) have been complimentary methods since cryo-EM was first applied to the field of structural biology. The relationship started by biasing structural models to fit low-resolution cryo-EM maps of large macromolecular complexes not amenable to crystallization. The connection between cryo-EM and MD evolved as cryo-EM maps improved in resolution, allowing advanced sampling algorithms to simultaneously refine backbone and sidechains. Moving beyond a single static snapshot, modern inferencing approaches integrate cryo-EM and MD to generate structural ensembles from cryo-EM map data or directly from the particle images themselves. We summarize the recent history of MD innovations in the area of cryo-EM modeling. The merits for the myriad of MD based cryo-EM modeling methods are discussed, as well as, the discoveries that were made possible by the integration of molecular modeling with cryo-EM. Lastly, current challenges and potential opportunities are reviewed.

Biochemistry & Molecular Biology↗

DOE OSTI2022

How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections?

Abstract We present an intercomparison of a suite of high‐resolution downscaled climate projections based on a six‐member General Circulation Model (GCM) ensemble from Coupled Models Intercomparison Project (CMIP6). The CMIP6 GCMs have been downscaled using dynamical and statistical downscaling techniques based on two meteorological reference observations over the conterminous United States. We use the regional climate model, RegCM4, for dynamical downscaling, double bias correction constructed analogs method for statistical downscaling, and Daymet and Livneh datasets as the reference observations for statistical training and bias‐correction. We evaluate the performances of downscaled data in both historical and future periods under the SSP585 scenario. While dynamical downscaling improves the simulation of some performance evaluation indices, it adds an extra bias in others, highlighting the need for statistical correction before its use in impact assessments. Downscaled datasets after bias‐correction compare exceptionally well with observations. However, the choice of downscaling techniques and the underlying reference observations influence the hydroclimate characteristics of downscaled data. For instance, the statistical downscaling generally preserves the GCMs climate change signal but overestimates the frequency of hot extremes. Similarly, simulated future changes are sensitive to the choice of reference observations, particularly for precipitation extremes that exhibit a higher projected increase in the ensembles trained and/or corrected by Daymet than Livneh. Overall, these results demonstrate that multiple factors, including downscaling techniques and reference observations, can substantially influence the outcome of downscaled climate projections and stress the need for a comprehensive understanding of such method‐based uncertainties.

54 ENVIRONMENTAL SCIENCES↗