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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

World beyond the nearest neighbors

The structure beyond the nearest neighbor atoms in liquid and glass is characterized by the medium-range order (MRO). In the conventional approach, the MRO is considered to result directly from the short-range order (SRO) in the nearest neighbors. To this bottom–up approach starting with the SRO, we propose to add a top–down approach in which global collective forces drive liquid to form density waves. The two approaches are in conflict with each other, and the compromise produces the structure with the MRO. The driving force to produce density waves provides the stability and stiffness to the MRO, and controls various mechanical properties. This dual framework provides a novel perspective for description of the structure and dynamics of liquid and glass.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

K-Nearest Neighbor (kNN)-based deep learning methods have been applied to many applications due to their simplicity and geometric interpretability. However, the robustness of kNN-based deep classification models has not been thoroughly explored and kNN attack strategies are underdeveloped. In this paper, we first propose an Adversarial Soft kNN (ASK) loss for developing more effective kNN-based deep neural network attack strategies and designing better defense methods against them. Our ASK loss provides a differentiable surrogate of the expected kNN classification error. It is also interpretable as it preserves the mutual information between the perturbed input and the in-class-reference data. We use the ASK loss to design a novel attack method called the ASK-Attack (ASK-Atk), which shows superior attack efficiency and accuracy degradation relative to previous kNN attacks on hidden layers. We then derive an ASK-Defense (ASK-Def) method that optimizes the worst-case ASK training loss. Experiments on CIFAR-10 (ImageNet) show that (i) ASK-Atk achieves ≥13% (≥ 13% ) improvement in attack success rate over previous kNN attacks, and (ii) ASK-Def outperforms the conventional adversarial training method by ≥ 6.9% (≥ 3.5% ) in terms of robustness improvement. Relevant codes are available at https://github.com/wangren09/ASK .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Delaunay walk for fast nearest neighbor: accelerating correspondence matching for ICP

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation.

3d point cloud processing↗
DOE OSTI2025

Spatiotemporal Downscaling Model for Solar Irradiance Forecast Using Nearest-Neighbor Random Forest and Gaussian Process

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs.

Asiedu, Shadrack (ORCID:0009000646004826)↗
DOE OSTI2023

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or “suspects,” were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer’s brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Detection of spatial clustering in the 1000 richest SDSS DR8 redMaPPer clusters with nearest neighbor distributions

ABSTRACT Distances to the k-nearest-neighbor (kNN) data points from volume-filling query points are a sensitive probe of spatial clustering. Here, we present the first application of kNN summary statistics to observational clustering measurement, using the 1000 richest redMaPPer clusters (0.1 ≤ z ≤ 0.3) from the SDSS DR8 catalog. A clustering signal is defined as a difference in the cumulative distribution functions (CDFs) of kNN distances from fixed query points to the observed clusters versus a set of unclustered random points. We find that the k = 1, 2-NN CDFs of redMaPPer deviate significantly from the randoms’ across scales of 35 to 155 Mpc, which is a robust signature of clustering. In addition to kNN, we also measure the two-point correlation function for the same set of redMaPPer clusters versus random points, which shows a noisier and less significant clustering signal within the same radial scales. Quantitatively, the χ2 distribution for both the kNN-CDFs and the two-point correlation function measured on the randoms peak at χ2 ∼ 50 (null hypothesis), whereas the kNN-CDFs (χ2 ∼ 300, p = 1.54 × 10−36) pick up a much more significant clustering signal than the two-point function (χ2 ∼ 100, p = 1.16 × 10−6) when measured on redMaPPer. Finally, the measured 3NN and 4NN CDFs deviate from the predicted k = 3, 4-NN CDFs assuming an ideal Gaussian field, indicating a non-Gaussian clustering signal for redMaPPer clusters, although its origin might not be cosmological due to observational systematics. Therefore, kNN serves as a more sensitive probe of clustering complementary to the two point correlation function, providing a novel approach for constraining cosmology and galaxy–halo connection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Block Mott insulating state induced by next-nearest-neighbor hopping in the S = 3 2 zigzag chain BaCoTe 2 O 7

Quasi-one-dimensional correlated electronic multiorbital systems with either ladder or chain geometries continue attracting considerable interest due to their complex electronic phases arising from the interplay of the hopping matrix, the crystal-field splitting, the electronic correlations (Hubbard repulsion U and Hund coupling J H ), and strong quantum fluctuations. Recently, the intriguing cobalt zigzag chain system BaCoTe 2 ⁢ O 7 , with electronic density n=7, was prepared experimentally. Here, we systematically study the electronic and magnetic properties of this quasi-one-dimensional compound from the theoretical perspective. Based on first-principles density functional theory calculations, strongly anisotropic one-dimensional electronic Co 3⁢d bands were found near the Fermi level. By evaluating the relevant hopping amplitudes, we provide the magnitude and origin of the nearest-neighbor (NN) and next-nearest-neighbor (NNN) hopping matrices in BaCoTe 2 ⁢ O 7 . With this information, we constructed a three-orbital electronic Hubbard model for this zigzag chain system, and studied two cases: with only a NN hopping matrix, and with NN plus NNN hopping matrices. Introducing the Hubbard and Hund couplings and studying the model via the density matrix renormalization group method, we constructed the ground-state phase diagram. A robust staggered ↑ - ↓ - ↑ - ↓ antiferromagnetic (AFM) region was found when only the NN hopping matrix in the chain direction was employed. However, for the realistic case where the NNN hopping matrix is also included, the dominant state becomes instead a block AFM ↑ - ↑ - ↓ - ↓ order, in agreement with experiments. The system displays Mott insulator characteristics with three half-filled orbitals, when the block AFM order is stable. In conclusion, our results for BaCoTe 2 ⁢O 7 provide guidance to experimentalists and theorists working on this zigzag one-dimensional chain and related materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Effects of the next-nearest-neighbor hopping on the low-dimensional Hubbard model: ferromagnetism, antiferromagnetism, and superconductivity

The Hubbard model has attracted considerable interest due to its prototypical role in describing strongly interacting electronic systems, such as high-critical-temperature superconductors as well as many novel quantum materials. By introducing next-nearest-neighbor (NNN) hoppings to the Hubbard model, the phase diagram becomes richer, and fascinating phenomena arise in both, one-dimensional chains and square lattices, such as: antiferromagnetism, ferromagnetism, superconductivity (SC), as well as charge orders, among others. Moreover, NNN hoppings play a fundamental role in understanding effects of doping on magnetism and pairing orders in strongly interacting regimes. In this article, we review the recent progress in understanding the different competing phases of this model in one and two dimensions from a computational perspective. In conclusion, we comment on the pressing technical challenges, illustrate the controversial results concerning the emergence of the SC phase, and conclude with our perspectives on future explorations.

Hubbard model↗
DOE OSTI2023

Study of beyond nearest-neighbor environment and intermediate-range order in a sodium aluminosilicate geopolymer using Reverse Monte Carlo modeling

A large scale three-dimensional structural model of a geopolymer with an approximate composition of NaAlSi 2 O 6 .5.5H 2 O obtained by Reverse Monte Carlo (RMC) modeling based on experimental high-energy x-ray diffraction is presented for the first time in order to obtain information regarding beyond nearest-neighbor environment and intermediate-range order (IRO). RMC model exhibits a three-dimensional network consisting of randomly cross-linked AlO 4 and SiO 4 tetrahedral units with uniformly distributed Na atoms and H 2 O molecules. The bond angle distributions, i.e. T-O-T, O-T-O and Na-O-Na angles, are somewhat distorted with lower values compared to analogous crystal systems. The origin of the first peak in the structure factor indicating IRO is investigated using the partial structure factors; Na-Na, Si-Na, Al-Si, O-H, and H-H atom pairs are found to be the main contributors. Ring size distribution analysis demonstrates that the structure mainly involves 6-, 7- and 8-membered rings. Finally, the coherence length of these IRO characteristics is ~10.3 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Realizing symmetry-protected topological phases in a spin-1/2 chain with next-nearest-neighbor hopping on superconducting qubits

Quantum simulation on near-term quantum hardware is a topic of intense interest. The preparation of novel quantum states of matter provides a quantitative assessment of the capabilities of near-term digital quantum computers to implement circuits with structure of relevance to quantum simulation. Here, we conduct a benchmark study by realizing symmetry-protected topological (SPT) phases of a spin-1/2 Hamiltonian with next-nearest-neighbor hopping on up to 11 qubits on a programmable superconducting quantum processor using adiabatic state preparation. Using recompilation techniques to reduce the gate count to around 50 two-qubit gates, we observe clear signatures of the two distinct SPT phases, such as excitations localized to specific edges and finite string-order parameters. Finally, we identify a parasitic phase associated with the two-qubit gate as the dominant imperfection that limits the depth of the circuits, indicating a research topic of interest for future hardware development.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Exact solution of the frustrated Potts model with next-nearest-neighbor interactions in one dimension via AI bootstrapping

The one-dimensional (1D) 𝐽 1 −𝐽 2 𝑞-state Potts model is solved exactly for arbitrary 𝑞 by analytically block-diagonalizing the original 𝑞 2 ×𝑞 2 transfer matrix into a simple 2 × 2 maximally symmetric subspace, based on using OpenAI's reasoning model o3-mini-high to exactly solve the 𝑞 = 3 case. Furthermore, by matching relevant subspaces, we map the Potts model onto a simpler effective 1D 𝑞-state Potts model, where 𝐽 2 acts as the nearest-neighbor interaction and 𝐽 1 as an effective magnetic field, nontrivially generalizing a 56-year-old theorem previously limited to the simplest case (𝑞 = 2, the Ising model). Our exact results provide insights to phenomena such as atomic or electronic order stacking in layered materials and the emergence of dome-shaped phases in complex phase diagrams. In conclusion, this work is anticipated to fuel both research in 1D frustrated magnets for recently discovered finite-temperature application potentials and the fast moving topic area of AI in science.

1-dimensional spin chains↗
DOE OSTI2024

Tilted stripes origin in La 1.88 Sr 0.12 CuO 4 revealed by anisotropic next-nearest neighbor hopping

Spin- and charge- stripe order has been extensively studied in the superconducting cuprates, among which underdoped La 2–x Sr x CuO 4 (LSCO) is an archetype with static spin stripes at low temperatures. An intriguing, but not completely understood, phenomenon in LSCO is that the stripes are tilted away from the high-symmetry Cu-Cu directions. Using high-resolution neutron scattering on LSCO with x = 0.12, we find two coexisting phases at low temperatures, one with static spin stripes and the other with fluctuating ones, both sharing the same tilt angle. Our numerical calculations using the doped Hubbard model elucidate the tilting’s origin, attributing it to anisotropic next-nearest neighbor hopping t', consistent with the material’s slight orthorhombicity. Our results underscore the model’s success in describing specific details of the ground state of this real material and highlight the role of t' in the Hamiltonian, revealing the delicate interplay between stripes and superconductivity across theoretical and experimental contexts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation

Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Anomalous lattice thermal conductivity increase with temperature in cubic GeTe correlated with strengthening of second-nearest neighbor bonds

Understanding thermal transport mechanisms in phase change materials is critical to elucidating the microscopic picture of phase transitions and advancing thermal energy conversion and storage. Experiments consistently show that cubic phase germanium telluride (GeTe) has an unexpected increase in lattice thermal conductivity with rising temperature. Despite its ubiquity, resolving its origin has remained elusive. In this work, we carry out temperature-dependent lattice thermal conductivity calculations for cubic GeTe through efficient, high-order machine-learned models and additional corrections for coherence effects. We corroborate the calculated phonon properties with our inelastic X-ray scattering measurements. Our calculated lattice thermal conductivity values agree well with experiments and show a similar increasing trend. Through additional bonding strength calculations, we propose that a major contributor to the increasing lattice thermal conductivity is the strengthening of second-nearest neighbor interactions. The findings herein serve to deepen our understanding of thermal transport in phase-change materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An approach to nearest neighbor analysis of pigment-protein complexes using chemical cross-linking in combination with mass spectrometry

Herein, protein cross-linking is the process of chemically joining two amino acids in a protein or protein complex by a covalent bond. When combined with mass spectrometry, it becomes one of the structural mass spectrometry techniques gaining in importance for deriving valuable three-dimensional structural information on proteins and protein complexes. This platform complements existing structural methods, such as NMR spectroscopy, X-ray crystallography, and cryo-EM. Photosynthetic pigment protein complexes serve as light-energy harvesting systems and perform photochemical conversion as part of the “early events” of photosynthesis. This chapter outlines how to prepare cross-linking pigment protein complex samples for LC-MS/MS analysis, including identification of the cross-linked species, network analysis in a protein complex, and structural modeling and justification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Structure-Function and Nearest Neighbor Interactions of the Cytochrome b 6 f Complex

Test prediction from our (i) high resolution (2.5 Å) structure of cyanobacterial cytochrome b 6 f complex (Structure, 22, 1008-1015, 2014), and (ii) analysis of quinone/quinol traffic within the b 6 f complex (Biophys. J., 107, 1620-1628, 2014) concerning the rate-limiting step in the linear (non-cyclic) electron transport chain. It was concluded that it arises from access of plastoquinol, the electron/proton donor, to the high potential electron carriers, i. e., the Fe 2 S 2 and cytochrome f proteins. The directed motion of the quinol proceeds via a narrow channel on the lumen or electrochemically positive, p- (lumen) side of the b 6 f complex which was defined by crystal structures of the b 6 f complex with quinol analogue inhibitors (J. Mol. Biol., 370, 39-52, 2007).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗