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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Efficient graph representation framework for chemical molecule similarity tasks

Graph data has emerged in numerous scientific domains and machine learning techniques have been widely used for analysis and learning of diverse data for prediction and decision. Machine learning techniques can readily address complex problems by leveraging their structural information. But graphs cannot be directly used for existing machine learning algorithms unless encoded as vectors. The problem of efficient representation of graphs is a substantial challenge in graph machine learning. In this paper, we propose a novel two-stage framework for the representation of chemical molecule graphs based on the strengths of Graph Isomorphism Networks (GINs) and Siamese autoencoders. In the first stage, the GIN model is constructed and trained using the structural information of chemical molecule graphs. Node attributes, edge attributes, and edge indices are used as input data, while graph attributes are used as labels. The GIN model effectively captures the structural characteristics of graphs and can accurately predict graph attributes, i.e., molecular properties. It also generates Graph Embeddings, represented as vectors that encode the structural information of graphs. In the second stage, Graph Embedding vectors are further optimized for downstream similarity tasks while preserving the graph structural information. The Siamese autoencoder is constructed and trained, which reduces the dimensionality of the Graph Embedding vectors, while maximizing the preservation of structural information in the original high-dimensional vectors. The resulting low-dimensional Graph Embeddings can be effectively utilized for tasks such as approximate nearest neighbor search. The experimental results demonstrate the effectiveness of our proposed framework in accurately predicting graph similarity.

Ma, Jiaji↗
DOE OSTI2023

Anomalous Charge Transfer from Organic Ligands to Metal Halides in Zero-Dimensional [(C 6 H 5 ) 4 P] 2 SbCl 5 Enabled by Pressure-Induced Lone Pair-π Interaction

Low-dimensional (low-D) organic metal halide hybrids (OMHHs) have emerged as fascinating candidates for optoelectronics due to their integrated properties from both organic and inorganic components. However, for most of low-D OMHHs, especially the zero-D (0D) compounds, the inferior electronic coupling between organic ligands and inorganic metal halides prevents efficient charge transfer at the hybrid interfaces and thus limits their further tunability of optical and electronic properties. Here, using pressure to regulate the interfacial interactions, efficient charge transfer from organic ligands to metal halides is achieved, which leads to a near-unity photoluminescence quantum yield (PLQY) at around 6.0 GPa in a 0D OMHH, [(C 6 H 5 ) 4 P] 2 SbCl 5 . In situ experimental characterizations and theoretical simulations reveal that the pressure-induced electronic coupling between the lone-pair electrons of Sb 3+ and the π electrons of benzene ring (lp-π interaction) serves as an unexpected “bridge” for the charge transfer. Further, our work opens a versatile strategy for the new materials design by manipulating the lp-π interactions in organic–inorganic hybrid systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets

This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with minimal modifications to the standard VAE loss function by leveraging prior statistical knowledge through auxiliary variables. These variables guide the shaping of the latent space by aligning latent factors with learned auxiliary variables. We validate the efficacy of Aux-VAE through comparative assessments on multiple datasets, including astronomical simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A deep learning-based direct forecasting of CO 2 plume migration

Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
DOE OSTI2023

Insulator-to-metal phase transition in a few-layered MoSe 2 field effect transistor

The metal-to-insulator phase transition (MIT) in low-dimensional materials and particularly two-dimensional layered semiconductors is exciting to explore due to the fact that it challenges the prediction that a two-dimensional system must be insulating at low temperatures. Thus, the exploration of MITs in 2D layered semiconductors expands the understanding of the underlying physics. Here we report the MIT of a few-layered MoSe 2 field effect transistor under a gate bias (electric field) applied perpendicular to the MoSe 2 layers. With low applied gate voltage, the conductivity as a function of temperature from 150 K to 4 K shows typical semiconducting to insulating character. Above a critical applied gate voltage, V c , the conductivity becomes metallic (i.e., the conductivity increases continuously as a function of decreasing temperature). Evidence of a metallic state was observed using an applied gate voltage or, equivalently, increasing the density of charge carriers within the 2D channel. We analyzed the nature of the phase transition using percolation theory, where conductivity scales with the density of charge carriers as σ ∝ (n - n c ) δ . The critical exponent for a percolative phase transition, δ(T), has values ranging from 1.34 (at T = 150 K) to 2 (T = 20 K), which is close to the theoretical value of 1.33 for percolation to occur. Thus we conclude that the MIT in few-layered MoSe 2 is driven by charge carrier percolation. Finally, the conductivity does not scale with temperature, which is a hallmark of a quantum critical phase transition.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Unified analysis of finite-size error for periodic Hartree-Fock and second order Møller-Plesset perturbation theory

Despite decades of practice, finite-size errors in many widely used electronic structure theories for periodic systems remain poorly understood. For periodic systems using a general Monkhorst-Pack grid, there has been no comprehensive and rigorous analysis of the finite-size error in the Hartree-Fock theory (HF) and the second order Møller-Plesset perturbation theory (MP2), which are the simplest wavefunction based method, and the simplest post-Hartree-Fock method, respectively. Such calculations can be viewed as a multi-dimensional integral discretized with certain trapezoidal rules. Due to the Coulomb singularity, the integrand has many points of discontinuity in general, and standard error analysis based on the Euler-Maclaurin formula gives overly pessimistic results. The lack of analytic understanding of finite-size errors also impedes the development of effective finite-size correction schemes. We propose a unified analysis to obtain sharp convergence rates of finite-size errors for the periodic HF and MP2 theories. Our main technical advancement is a generalization of the result of Lyness [Math. Comp. 30 (1976), pp. 1–23] for obtaining sharp convergence rates of the trapezoidal rule for a class of non-smooth integrands. Our result is applicable to three-dimensional bulk systems as well as low dimensional systems (such as nanowires and 2D materials). Our unified analysis also allows us to prove the effectiveness of the Madelung-constant correction to the Fock exchange energy, and the effectiveness of a recently proposed staggered mesh method for periodic MP2 calculations (see X. Xing, X. Li, and L. Lin [J. Chem. Theory Comput. 17 (2021), pp. 4733–4745]). In conclusion, our analysis connects the effectiveness of the staggered mesh method with integrands with removable singularities, and suggests a new staggered mesh method for reducing finite-size errors of periodic HF calculations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

TOPEX/POSEIDON tides estimated using a global inverse model

Altimetric data from the TOPEX/POSEIDON mission will be used for studies of global ocean circulation and marine geophysics. However, it is first necessary to remove the ocean tides, which are aliased in the raw data. The tides are constrained by the two distinct types of information: the hydrodynamic equations which the tidal fields of elevations and velocities must satisfy, and direct observational data from tide gauges and satellite altimetry. Here we develop and apply a generalized inverse method, which allows us to combine rationally all of this information into global tidal fields best fitting both the data and the dynamics, in a least squares sense. The resulting inverse solution is a sum of the direct solution to the astronomically forced Laplace tidal equations and a linear combination of the representers for the data functionals. The representer functions (one for each datum) are determined by the dynamical equations, and by our prior estimates of the statistics or errors in these equations. Our major task is a direct numerical calculation of these representers. This task is computationally intensive, but well suited to massively parallel processing. By calculating the representers we reduce the full (infinite dimensional) problem to a relatively low-dimensional problem at the outset, allowing full control over the conditioning and hence the stability of the inverse solution. With the representers calculated we can easily update our model as additional TOPEX/POSEIDON data become available. As an initial illustration we invert harmonic constants from a set of 80 open-ocean tide gauges. We then present a practical scheme for direct inversion of TOPEX/POSEIDON crossover data. We apply this method to 38 cycles of geophysical data records (GDR) data, computing preliminary global estimates of the four principal tidal constituents, M(sub 2), S(sub 2), K(sub 1) and O(sub 1). The inverse solution yields tidal fields which are simultaneously smoother, and in better agreement with altimetric and ground truth data, than previously proposed tidal models. Relative to the 'default' tidal corrections provided with the TOPEX/POSEIDON GDR, the inverse solution reduces crossover difference variances significantly (approximately 20-30%), even though only a small number of free parameters (approximately equal to 1000) are actually fit to the crossover data.

Egbert, Gary D.↗
DOE OSTI2026

Pseudo–Jahn–Teller Distortion in a One-Dimensional π-Conjugated Polymer

Structural distortions in low-dimensional π-conjugated systems profoundly influence their electronic properties, but the control of such behavior in laterally extended systems remains challenging. Here, in this study, we demonstrate that a one-dimensional conjugated polymer─ poly -(difluorenoheptalene-ethynylene) (PDFHE)─undergoes a pronounced out-of-plane backbone distortion, equivalent to a spontaneous symmetry breaking (SSB) of its mirror symmetry. We synthesized PDFHE on noble metal surfaces and characterized its structure and electronic states using low-temperature scanning tunneling microscopy (STM). Rather than adopting a planar, high-symmetry conformation, PDFHE relaxes into nonplanar isomers stabilized by a pseudo–Jahn–Teller (PJT) distortion having mirror-odd out-of-plane character. The distortion lowers the total energy and increases the band gap, providing a concise rationale for the observed symmetry breaking. Density functional theory calculations corroborate these findings, providing a microscopic explanation for the SSB. Our results show that even in mechanically robust extended π-systems, subtle electron–lattice coupling can spontaneously drive significant structural rearrangements, even in mechanically robust extended π-systems.

conformation↗
DOE OSTI2021

Zero-Dimensional Hybrid Organic–Inorganic Indium Bromide with Blue Emission

Low-dimensional hybrid organic–inorganic metal halides have received increased attention because of their outstanding optical and electronic properties. However, the most studied hybrid compounds contain lead and have long-term stability issues, which must be addressed for their use in practical applications. In this work, we report a new zero-dimensional hybrid organic–inorganic halide, RInBr 4 , featuring photoemissive trimethyl(4-stilbenyl)methylammonium (R + ) cations and nonemissive InBr 4 – tetrahedral anions. The crystal structure of RInBr 4 is composed of alternating layers of inorganic anions and organic cations along the crystallographic a axis. The resultant hybrid demonstrates bright-blue emission with Commission Internationale de l’Eclairage color coordinates of (0.19, 0.20) and a high photoluminescence quantum yield (PLQY) of 16.36% at room temperature, a 2-fold increase compared to the PLQY of 8.15% measured for the precursor organic salt RBr. On the basis of our optical spectroscopy and computational work, the organic component is responsible for the observed blue emission of the hybrid material. In addition to the enhanced light emission efficiency, the novel hybrid indium bromide demonstrates significantly improved environmental stability. These findings may pave the way for the consideration of hybrid organic In(III) halides for light emission applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Learning Canonical Embeddings for Unsupervised Shape Correspondence With Locally Linear Transformations

We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE)-originally designed for nonlinear dimensionality reduction-for shape correspondence. The key idea is to find dense correspondences between shapes by first obtaining high-dimensional neighborhood-preserving embeddings of low-dimensional point clouds and subsequently aligning the source and target embeddings using locally linear transformations. We demonstrate that learning the embedding using a new LLE-inspired point cloud reconstruction objective results in accurate shape correspondences. More specifically, the approach comprises an end-to-end learnable framework of extracting high-dimensional neighborhood-preserving embeddings, estimating locally linear transformations in the embedding space, and reconstructing shapes via divergence measure-based alignment of probability density functions built over reconstructed and target shapes. Our approach enforces embeddings of shapes in correspondence to lie in the same universal/canonical embedding space, which eventually helps regularize the learning process and leads to a simple nearest neighbors approach between shape embeddings for finding reliable correspondences. Comprehensive experiments show that the new method makes noticeable improvements over state-of-the-art approaches on standard shape correspondence benchmark datasets covering both human and nonhuman shapes.

deformation↗
NASA NTRS2022

Deep Interacting Multiple Model Filtering

In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.

Ghananeel Rotithor↗
DOE OSTI2024

Thermally induced structural evolution and nanoscale interfacial dynamics in Bi-Sb-Te layered nanostructures

Layered chalcogenides, including Bi-Sb-Te ternary alloys and heterostructures, are renowned as thermoelectric and topological insulators and have recently been highlighted as plasmonic building blocks beyond noble metals. Here, we conduct joint in situ transmission electron microscopy and density functional theory calculations to investigate the temperature-dependent nanoscale dynamics and interfacial properties, identifying the role of native defects and edge configurations in the anisotropic sublimation of Bi 2 Te 3 -Sb 2 Te 3 heterostructures and Sb 2-x Bi x Te 3 alloys. We report structural dynamics, including edge evolution, layer-by-layer sublimation, and the formation and coalescence of thermally induced polygonal nanopores. These nanopores are initiated by preferential dissociation of tellurium, reducing thermal stability in heterostructures. Triangular and quasi-hexagonal configurations dominate nanopore structures in heterostructures. Our calculations reveal antisite defects (Te Sb and Te Bi ) as key players in defect-assisted sublimation. These findings enhance our understanding of nanoscale dynamics and assist in designing tunable low-dimensional chalcogenides.

Bi2Te3-Sb2Te3 heterostructure↗
DOE OSTI2024

Synthesis and growth of solution-processed chiral perovskites

In materials science, chiral perovskites stand out due to their exceptional optoelectronic properties and the versatility in their structure and composition, positioning them as crucial in the advances of technologies in spintronics and chiroptical systems. This review underlines the critical role of synthesizing and growing these materials, a process integral to leveraging their complex interplay between structural chirality and distinctive optoelectronic properties, including chiral-induced spin selectivity and chiroptical activity. The paper offers a comprehensive summary and discussion of the methods used in the synthesis and growth of chiral perovskites, delving into extensive growth techniques, fundamental mechanisms, and strategic approaches for the engineering of low-dimensional perovskites, alongside the creation of novel chiral ligands. The necessity of developing new synthetic approaches and maintaining precise control during the growth of chiral perovskites is emphasized, aiming to enhance their structural chirality and boost their efficiency in spin and chiroptical selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Density-independent plasmons for terahertz-stable topological metamaterials

Significance Plasmons, collective electron density oscillations in metals or doped semiconductors, provide an efficient way to couple light and matter at subwavelength scales. Usually, plasmons are carrier density ( n ) dependent, which may cause unstable functionalities of plasmons, especially for cutting-edge terahertz technology. Here, we reveal a unified mechanism for generating unusual n -independent plasmons (DIPs), where terahertz DIPs can be excited in some low-dimensional topological semimetals. Stable terahertz spectroscopy from narrowband to broadband and even a quantization can be achieved in topological metamaterials. Our study not only broadens the concept of plasmons by combining with exotic topological states but also paves a way to designing a compact device for stable terahertz applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Revealing the three-dimensional arrangement of polar topology in nanoparticles

In the early 2000s, low dimensional ferroelectric systems were predicted to have topologically nontrivial polar structures, such as vortices or skyrmions, depending on mechanical or electrical boundary conditions. A few variants of these structures have been experimentally observed in thin film model systems, where they are engineered by balancing electrostatic charge and elastic distortion energies. However, the measurement and classification of topological textures for general ferroelectric nanostructures have remained elusive, as it requires mapping the local polarization at the atomic scale in three dimensions. Here we unveil topological polar structures in ferroelectric BaTiO3 nanoparticles via atomic electron tomography, which enables us to reconstruct the full three-dimensional arrangement of cation atoms at an individual atom level. Our three-dimensional polarization maps reveal clear topological orderings, along with evidence of size-dependent topological transitions from a single vortex structure to multiple vortices, consistent with theoretical predictions. The discovery of the predicted topological polar ordering in nanoscale ferroelectrics, independent of epitaxial strain, widens the research perspective and offers potential for practical applications utilizing contact-free switchable toroidal moments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗