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

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NASA NTRS1992

Gravitational microlensing - The effect of a single lens and the impact of sparse sampling

Topics in gravitational microlensing are examined. One issue is that even in cases in which the surface mass density is quite low, microlensing events cannot be considered as caused by individual stars. Whenever a high-magnification event occurs, the quasar has crossed a caustic. These caustics, however, are complex structures even for low surface-mass densities. This is illustrated by removing a single low-mass star from an ensemble of microlenses. The magnification patterns and light curves are compared with and without this star. The differences are striking and much more complex than expected. The effect of sparse sampling on the perception of microlensed light curves is examined, and attention is given to the influence of poor time coverage on simulated light curves. A regular monitoring program for microlensing candidates (especially 2237 + 0305) with a dedicated telescope is needed to make observations in different wavebands each night.

Wambsganss, Joachim↗

DOE OSTI2025

All-optical photoluminescence response of nitrogen-vacancy ensembles in diamond at low magnetic fields

All-optical (AO) microwave-free magnetometry using nitrogen-vacancy (N-𝑉) centers in diamond is attractive due to its broad applicability and reduced experimental complexity. In this work, we investigate room-temperature AO photoluminescence (PL) at low magnetic fields ( <2 mT) using diamonds with N-𝑉 ensembles at parts-per-million (ppm) concentrations. The measured AO-PL contrast features as a function of the applied magnetic field magnitude and direction are correlated with near-degenerate N-𝑉 electronic spin and hyperfine transitions from different N-𝑉 orientations within the diamond host. Reasonable agreement is found between low-field AO-PL measurements and model-based simulations of the effects of resonant dipolar interactions between N-𝑉 centers. The maximum observed AO-PL contrast depends on both the N-𝑉 concentration and the laser-illumination intensity at 532 nm. These results imply different optimal conditions for low-field AO N-𝑉 sensing compared to conventional optically detected magnetic resonance (ODMR) techniques, suggesting new research and application opportunities using AO measurements with lower system complexity, size, weight, and power.

Dipolar interaction↗

NASA NTRSDate not supplied

Impact of GPM-retrieved surface meteorology condition on simulations of two winter storms during ICE-POP 2018

Led by the KMA as a component of the WMO's World Weather Research Program (WWRP) Research and Development and Forecast Demonstration Projects (RDP/FDP). Taken place during the Winter Olympics (February-March) of 2018. Goals of ICE-POP: To improve understanding on severe weathers (snowfalls, visibility, rapid wind changes and gusts,) over complex terrain; To improve the predictability of nowcasting and very-short range forecasting with a few kilometer horizontal resolution - Development of NWP-based nowcasting, multi-scale data assimilation and time-lagged ensemble for VSRF, and radar reflectivity and visibility data assimilation; To improve verification for high resolution model considering complex terrain.

Xuanli Li↗

DOE OSTI2024

FORESTR: Finding, Organizing, Representing, Explaining, Summarizing, and Thinning Random forests

Random forests have become popular models used for data driven predictions. As a result, random forests are currently used or being considered for high-consequence mission applications in national security, such as the prediction of yield from optical signals and malware detection. While random forests may provide accurate predictions, the complexity of the algorithm causes a lack of interpretability. Random forests are an ensemble of regression or decision trees. Individual regression and decision trees are interpretable, but ensembles are inherently difficult to interpret due to the compilation of many models. We aim to increase the interpretability of random forests by finding patterns in the ensemble of trees that can be used to “thin” (or remove) trees. As a starting point, in this report, we develop a new distance metric for quantifying the similarity between trees based on their topologies (i.e., shapes). We base the metric on a novel distance metric for graphs that is a proper mathematical distance, is invariant to transformations, has registration between graphs, and computes topological evolutions between graphs. We use the tree distance metric to compute tree statistics such as a “mean tree” and to identify clusters of trees. We apply the developed methodology to a toy dataset and a mission relevant product inspection dataset to demonstrate how the metric can provide insight into random forests. Furthermore, we discuss the limitations of the approach and ideas for future research into how the metric could be used as a thinning tool to develop less complex models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Metastable Clusters and Competitive Solvation Tune Ion Pairing at Liquid Interfaces

The balance of hydrophobic and hydrophilic interactions underlies emergent phenomena in complex multicomponent chemical systems. Here, we show that a supposedly ‘non–interacting’ nonpolar phase can be used to competitively solvate amphiphilic molecules at an oil/aqueous interface. This solvation, as probed by surface specific nonlinear spectroscopy and simulations, results in a molecularly thin corrugated phase boundary featuring metastable assemblies that alter the hydrogen bonding networks of water and the apparent ‘hard/soft’ descriptors used to describe ionic interactions. We show that competitive solvation enhances amphiphile mobility, opening up otherwise energetically inaccessible complexes that transiently interact with aqueous phase ions. These transient species impact ensemble binding affinities and may represent the molecular agents responsible for aspects of ionic transport and function. In conclusion, the result of this work highlights how seemingly unrelated nonpolar interactions feedback onto aqueous phase chemical phenomena, providing a pathway to tune phase separation and self-assembly to access new reaction pathways using interfaces for a range of chemical and biological systems.

Anions↗

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

Artificial Intelligence Designer of Materials and Processes for Advanced Power Generation

In this presentation, ‘deep-freeze’ graphs, ‘convoluted filtering’ networks, ‘mirror-image’ graphs, and adversarial ensemble methods are utilized to support inversion modeling for optimization of the complex compositions and complex processes in design of high-performing alloys, with their properties tailored to the energy application specifications.

Romanov, Vyacheslav↗

DOE OSTI2024

GPU Acceleration of a Diagnostic Wind Solver

Reducing QUIC-Fire simulation runtimes is crucial in enabling simulation ensembles to guide science-driven prescribed fire planning in regions of complex terrain. Utilizing GPUs through OpenACC and Kokkos frameworks to accelerate generation of 3D windfields in QUIC-Fire would provide substantial speedup to the runtime of the code. Using GPUs for the Successive Over-Relaxation (SOR) algorithm utilized in QUIC-Fire will require deriving a ‘four colored’ version of the well explored ‘Red-Black’ SOR parallelization scheme. This effort could open the door for utilizing GPUs in other portions of the QUIC-Fire algorithm, increasing its viability in more complex and larger domains.

58 GEOSCIENCES↗

DOE OSTI2023

Dataset for SIAM MPI23 Project "Model inversion for complex physical systems using low-dimensional surrogates"

Training and testing data for the SIAM MPI23 workshop "Model inversion for complex physical systems using low-dimensional surrogates". The data consists of ensembles of input and output pairs corresponding to queries of a 2D saturated groundwater flow model. The inputs consist of random vectors which map to discretized model parameter fields via a Kosambi-Karhunen-Loève expansion. Output corresponds to discretized pressure fields.

basis adaptation, conditional Karhunen-Loève expan↗

DOE OSTI2020

Efficient Distance-based Global Sensitivity Analysis for Terrestrial Ecosystem Modeling

Sensitivity analysis in terrestrial ecosystem modeling is important for understanding controlling processes, guiding model development, and targeting new observations to reduce parameter and prediction uncertainty. Complex and computationally expensive terrestrial ecosystem models (TEM) limit the number of ensemble simulations, requiring sophisticated and efficient methods to analyze sensitivities of multiple model responses to different types of parameter uncertainties. In this study, we propose a distance-based global sensitivity analysis (DGSA) method. DGSA first classifies model response samples into a small set of discrete classes and then calculates the distance between parameter frequency distributions in different classes to measure the parameter sensitivity. The principle is that, if the parameter distribution is the same in each class, then the model response is insensitive to the parameter, while a large difference in the distributions indicates the parameter is influential to the response. Built on this idea, DGSA can be applied to analyze sensitivity of a single and a group of responses to different kinds of parameter uncertainties including continuous, discrete and even stochastic. Besides the main-effect sensitivity from a single parameter, DGSA can also quantify the sensitivity from parameter interactions. Additionally, DGSA is computationally efficient which can use a small number of model evaluations to obtain an accurate and statistically significant result. We applied DGSA to two TEMs, one having eight parameters and three kinds of model responses, and the other having 47 parameters and a long-period response. We demonstrated that DGSA can be used for sensitivity problems with multiple responses and high-dimensional parameters efficiently.

Lu, Dan↗

DOE OSTI2020

A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification

A novel modeling framework that simultaneously improves accuracy, predictability, and computational efficiency is presented. It embraces the benefits of three modeling techniques integrated together for the first time: surrogate modeling, parameter inference, and data assimilation. The use of polynomial chaos expansion (PCE) surrogates significantly decreases computational time. Parameter inference allows for model faster convergence, reduced uncertainty, and superior accuracy of simulated results. Ensemble Kalman filters assimilate errors that occur during forecasting. To examine the applicability and effectiveness of the integrated framework, we developed 18 approaches according to how surrogate models are constructed, what type of parameter distributions are used as model inputs, and whether model parameters are updated during the data assimilation procedure. We conclude that (1) PCE must be built over various forcing and flow conditions, and in contrast to previous studies, it does not need to be rebuilt at each time step; (2) model parameter specification that relies on constrained, posterior information of parameters (so-called Selected specification) can significantly improve forecasting performance and reduce uncertainty bounds compared to Random specification using prior information of parameters; and (3) no substantial differences in results exist between single and dual ensemble Kalman filters, but the latter better simulates flood peaks. The use of PCE effectively compensates for the computational load added by the parameter inference and data assimilation (up to ~80 times faster). Therefore, the presented approach contributes to a shift in modeling paradigm arguing that complex, high-fidelity hydrologic and hydraulic models should be increasingly adopted for real-time and ensemble flood forecasting.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

The Kinetics of Electron Transfer from CdS Nanorods to the MoFe Protein of Nitrogenase

Combining the remarkable catalytic properties of redox enzymes with highly tunable light absorbing properties of semiconductor nanocrystals enables the light-driven catalysis of complex, multielectron redox reactions. This work focuses on systems that combine CdS nanorods (NRs) with the MoFe protein of nitrogenase to drive photochemical N 2 reduction. We used transient absorption spectroscopy (TAS) to examine the kinetics of electron transfer (ET) from CdS NRs to the MoFe protein. For CdS NRs with dimensions similar to those previously used for photochemical N 2 reduction, the rate constant for ET from CdS NRs competes with other electron relaxation processes, such that when a MoFe protein is bound to a NR, about one-half of the photoexcited electrons are delivered to the enzyme. The NR-MoFe protein binding is incomplete with more than one-half of the NRs in solution not having a MoFe protein bound to accept electrons. The quantum efficiency of ET (QE ET ) in these ensemble samples is similar to previously reported efficiencies of product (NH 3 and H 2 ) formation, suggesting that the enzyme utilizes the delivered electrons without major loss pathways. Our analysis suggests that QE ET , and therefore the photochemical product formation, is limited at the ensemble level by the NR-MoFe protein binding and at the single-complex level by the competitiveness of ET. We characterized ET kinetics for several CdS NRs samples with varying dimensions and found that for CdS NRs with an average diameter of 4.2 nm the ET efficiency dropped to undetectable levels, defining a maximum NR diameter that should be used to photochemically drive the MoFe protein. The work described here provides insights into the design of systems with increased control of photochemical N 2 reduction catalyzed by the MoFe protein of nitrogenase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Efficient Unitary Designs from Random Sums and Permutations

A unitary k-design is an ensemble of unitaries that matches the first k moments of the Haar measure. In this work, we provide two efficient constructions of k-designs on n-qubits using new random matrix theory techniques. Our first construction is based on exponentiating sums of random i.i.d. Hermitian matrices and uses O(k2n2)-many gates. In the spirit of central limit theorems, we show that this random sum approximates the Gaussian Unitary Ensemble (GUE). We then show that the product of just two exponentiated GUE matrices is already approximately Haar random. Our second construction is based on products of exponentiated sums of random permutations and uses Õ(k poly (n)) many gates. The k dependence is optimal (up to polylogarithmic factors) and is inherited from the efficiency of existing k-wise independent permutations. Furthermore, replacing random permutations with quantum-secure pseudorandom permutations (PRPs), we also obtain a pseudorandom unitary (PRU) ensemble that is secure under nonadaptive queries. A central feature of both proofs is a new connection between the polynomial method in quantum query complexity and the large-dimension (N) expansion in random matrix theory. In particular, the first construction uses the polynomial method to control high moments of certain random matrix ensembles without requiring delicate Weingarten calculations. In doing so, we define and solve a moment problem on the unit circle, asking whether a finite number of equally weighted points can reproduce a given set of moments. In our second construction, the key step is to exhibit an orthonormal basis for irreducible representations of the partition algebra that has a low-degree large-N expansion. This allows us to show that the distinguishing probability is a low-degree rational polynomial of the dimension N.

algebra↗

DOE OSTI2022

Calabi-Yau CFTs and random matrices

Using numerical methods for finding Ricci-flat metrics, we explore the spectrum of local operators in two-dimensional conformal field theories defined by sigma models on Calabi-Yau targets at large volume. Focusing on the examples of K3 and the quintic, we show that the spectrum, averaged over a region in complex structure moduli space, possesses the same statistical properties as the Gaussian orthogonal ensemble of random matrix theory.

superstring vacua↗

DOE OSTI2021

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Random Matrix Approach to Quantum Adiabatic Evolution Algorithms

We analyze the power of quantum adiabatic evolution algorithms (Q-QA) for solving random NP-hard optimization problems within a theoretical framework based on the random matrix theory (RMT). We present two types of the driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that the failure mechanism of the QAA is due to the interaction of the ground state with the "cloud" formed by all the excited states, confirming that in the driven RMT models. the Landau-Zener mechanism of dissipation is not important. We show that the QAEA has a finite probability of success in a certain range of parameters. implying the polynomial complexity of the algorithm. The second model corresponds to the standard QAEA with the problem Hamiltonian taken from the Gaussian Unitary RMT ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. However, the driven RMT model always leads to the exponential complexity of the algorithm due to the presence of the long-range intertemporal correlations of the eigenvalues. Our results indicate that the weakness of effective transitions is the leading effect that can make the Markovian type QAEA successful.

Boulatov, Alexei↗

DOE OSTI2022

Predicting Oxygen Off-Stoichiometry and Hydrogen Incorporation in Complex Perovskite Oxides

Chemically and structurally complex solid compounds, including those with significant off-stoichiometry, are rapidly extending new material functionality across a variety of applications. Accelerated development of these compounds requires accurate predictions of material defect properties including effective defect formation energies and equilibrium defect concentrations. Traditional first-principles approaches typically examine dilute defect concentrations and relatively ordered atomic structures to identify the lowest energy defect sites. These approaches are rarely suitable for describing the disorder present in these systems and its influence on defect formation, which can lead to unphysically large predictions for defect concentrations. Here, we demonstrate a new method to accurately predict the temperature and pressure dependence of oxygen vacancy concentrations and proton interstitial concentrations in complex oxides. This method extends standard dilute defect calculations to incorporate atomic and magnetic disorder, employs the ensemble descriptions of defect sites resulting in improved predictions of defect formation energies, and accounts for effects beyond the dilute defect limit. To demonstrate our method, we show that the predicted defect concentrations in perovskites used as ceramic fuel cell cathodes, including Ba0.5Sr0.5Fe0.8Zn0.2O3-d, Ba0.5Sr0.5Co0.8Fe0.2O3-d, and BaCo1-x-y-zFexZryYzO3-d, are in good agreement with experimental values, thereby opening the door for predictive design of complex oxides by these applications.

DFT↗

DOE OSTI2022

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

59 BASIC BIOLOGICAL SCIENCES↗