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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Kernel polynomial method for linear spin wave theory

Calculating dynamical spin correlations is essential for matching model magnetic exchange Hamiltonians to momentum-resolved spectroscopic measurements. A major numerical bottleneck is the diagonalization of the dynamical matrix, especially in systems with large magnetic unit cells, such as those with incommensurate magnetic structures or quenched disorder. In this paper, we demonstrate an efficient scheme based on the kernel polynomial method for calculating dynamical correlations of relevance to inelastic neutron scattering experiments. This method reduces the scaling of numerical cost from cubic to linear in the magnetic unit cell size.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Exciton polaron formation and hot-carrier relaxation in rigid Dion–Jacobson-type two-dimensional perovskites

The efficiency of two-dimensional Dion–Jacobson-type materials relies on the complex interplay between electronic and lattice dynamics; however, questions remain about the functional role of exciton–phonon interactions. Here we establish the robust polaronic nature of the excitons in these materials at room temperature by combining ultrafast spectroscopy and electronic structure calculations. We show that polaronic distortion is associated with low-frequency (30–60 cm –1 ) lead iodide octahedral lattice motions. More importantly, we discover how targeted ligand modification of this two-dimensional perovskite structure manipulates exciton–phonon coupling, exciton polaron population and carrier cooling. At high excitation density, stronger exciton–phonon coupling increases the hot-carrier lifetime, forming a hot-phonon bottleneck. Furthermore, our study provides detailed insight into the exciton–phonon coupling and its role in carrier cooling in two-dimensional perovskites relevant for developing emerging hybrid semiconductor materials with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Energy-efficient, Large-scale Molecular Dynamics Simulations via Hardware- and Algorithm-level Optimization

This work aims to develop a framework for energy-efficient computing that will enable molecular dynamics (MD) simulations of large-scale phenomena with atomic precision and simultaneously remove computational bottlenecks limiting the speed of MD simulations. We seek to implement such an approach through the development of surrogate models for the interatomic force calculation combined with the use of mixed numerical precision formats. For a model system of neutral atoms (only pairwise interactions), significant force calculation efficiency improvements were achieved, without detrimental effects on atomic structures or average energies, using single precision, by developing a surrogate model (deep neural network), and by quantizing this surrogate model. For a model system of charged atoms, the reciprocal-space calculation of electrostatic interactions was identified as the main bottleneck, and the development of a surrogate model should be pursued to achieve an estimated one-order-of-magnitude additional speedup.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Synthetic Accessibility and Sodium Ion Conductivity of the Na8–x A x P2O9 (NAP) High-Temperature Sodium Superionic Conductor Framework

Advancement of solid-state electrolytes (SSEs) for all solid-state batteries typically focuses on modification of a known structural framework to improve conductivity, e.g., cation substitution for an immobile ion or varying the concentration of the mobile ions. Novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, thereby unlocking optimal conductors with different properties. Herein, we perform a high-throughput survey of a structural framework for sodium ion conduction, Na8–x A x P2O9 (NAP), to understand the family’s thermodynamic stability, synthesizability, and ionic conduction. We show that the parent phase Na4TiP2O9 (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons arising from pseudo-Jahn–Teller mode in the 1D titanium chains. Screening compounds in which Ti is substituted by other metals computationally reveal a number of candidates that are predicted to be low in formation energy and have high predicted ionic conductivities. High-throughput experimental and subsequent methodology optimization trials deliver one new compound, Na4SnP2O9 (NSP). X-ray diffraction (XRD), microscopy, and spectroscopy characterization indicate that the room-temperature structure of NSP is similar to the high-temperature, orthorhombic NTP phase but with some small unresolved structural differences. These uncharacterized structural details are speculated to limit the ion conductivity. Temperature-dependent XRD and electrochemical impedance spectroscopy indicate multiple coupled conductivity–structure transitions at a high temperature. We demonstrate the challenges with synthesis development and a priori identification of promising SSE phases as a major bottleneck in new (energy) materials development.

Chemical reactions↗
DOE OSTI2022

A Mountaintop View Requires Minimal Sorting: A Faster Contour Tree Algorithm

Consider a scalar field f : M → R, where M is a triangulated simplicial mesh in R d . A level set, or contour, at value v is a connected component of f –1 (v). As v is changed, these contours change topology, merge into each other, or split. Contour trees are concise representations of f that track this contour behavior. The vertices of these trees are the critical points of f, where the gradient is zero. The edges represent changes in the topology of contours. It is a fundamental data structure in data analysis and visualization, and there is significant previous work (both theoretical and practical) on algorithms for constructing contour trees. Suppose M has n vertices, N facets, and t critical points. A classic result of Carr, Snoeyink, and Axen (2000) gives an algorithm that takes O(n log n+Nα(N)) time (where α(·) is the inverse Ackermann function). A further improvement to O(t log t + N) time was given by Chiang et al. All these algorithms involve a global sort of the critical points, a significant computational bottleneck. Unfortunately, lower bounds of Ω(t log t) also exist. We present the first algorithm that can avoid the global sort and has a refined time complexity that depends on the contour tree structure. Intuitively, if the tree is short and fat, we get significant improvements in running time. For a partition of the contour tree into a set of descending paths, P, our algorithm runs in O($\Sigma$ pϵP |p| log |p| + tα(t) + N). This is at most O(t log D + N), where D is the diameter of the contour tree. Moreover, it is O(tα(t) + N) for balanced trees, a significant improvement over the previous complexity. Our algorithm requires numerous ideas: partitioning the contour tree into join and split trees, a local growing procedure to iteratively build contour trees, and the use of heavy path decompositions for the time complexity analysis. There is a crucial use of a family of binomial heaps to maintain priorities, ensuring that any comparison made is between comparable nodes of the contour tree. We also prove lower bounds showing that the $\Sigma$ pϵP |p| log |p| complexity is inherent to computing contour trees.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

In Situ Probing Potassium-ion Intercalation-induced Amorphization in Crystalline Iron Phosphate Cathode Materials

Na-ion and K-ion batteries are promising alternatives for large-scale energy storage applications due to their abundance and low cost. Intercalation of these large ions could cause irreversible structural deformation and partial to complete amorphization in the crystalline electrodes. The designing of new amorphous electrodes is another route to develop electrodes to store these ions reversibly. Lack of understanding of dynamic changes in the amorphous nanostructures during battery operation is the bottleneck for further developments. Here, we report the utilization of in situ digital image correlation and in-operando X-ray diffraction (XRD) techniques to probe dynamic changes in the amorphous phase of iron phosphate during potassium intercalation. In-operando XRD demonstrates amorphization in the electrode’s nanostructure during the first charge/discharge cycle. Additionally, the ex-situ high-resolution transmission electron microscopy further confirms the amorphization after potassium insertion. In situ strain analysis detects the reversible deformation associated with redox reactions in the amorphous phases. Our approach offers new insights on the mechanisms of ion intercalation in the amorphous nanostructures which are highly potent for development of next-generation batteries.

Ozdogru, Bertan↗
DOE OSTI2023

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid–liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid–liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

GPU Profiling and Optimizing xRAGE (Final Report)

Our project’s objective is to increase the efficiency of GPU-enabled kernels in xRAGE. To do so, we conduct GPU profiling with NSight Systems on xRAGE tests unsplit_sod_1d and unsplit_sedov_2d to identify bottlenecks and understand the behavior of the GPU during code execution. Next, we analyze these generated GPU profiles to locate the lines of code whose optimization have the most potential for improving runtime. We replicate the structure of the code in smaller test problems that are easier to understand, edit, and run quickly. Within these test problems, we implement two different methods of improving performance: transformation of nested loops into a single MDRangePolicy and hierarchical parallelization using teams of threads. Both methods show speedups in the test code, and after transferring them to xRAGE, they both show up to 30x speedups on various computing platforms. Profiling the edited versions of xRAGE reveals that the GPU successfully executed the bottlenecks with greater efficiency

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enabling Extreme Fast-Charging: Challenges at the Cathode and Mitigation Strategies

We report charging lithium-ion batteries (LiBs) in 10 to 15 min via extreme fast-charging (XFC) is important for the widespread adoption of electric vehicles (EVs). Lately, the battery research community has focused on identifying XFC bottlenecks and determining novel design solutions. Like other LiB components, cathodes can present XFC bottlenecks, especially when considering long-term battery life. Therefore, it is necessary to develop a comprehensive understanding of how XFC conditions degrade LiB cathodes. The present article reviews relevant cathode-focused studies and summarizes the current understanding regarding cathode performance and aging issues under XFC conditions. Dominant aging modes and mechanisms are identified at different length-scales with electrochemical correlations for LiNi x Mn y Co z O 2 (NMC)-based cathodes. A range of electrochemical techniques and models provide key insights into cathode performance and life issues. A suite of multimodal and multiscale microscopy and X-ray techniques is surveyed to quantify chemical, structural, and crystallographic NMC-cathode degradation. Cathode cycle-life is scaled to equivalent EV miles to illustrate how cathode degradation translates to real-world scenarios and quantifies cathode-related bottlenecks that hinder XFC adoption. Finally, the article discusses several cathode cycle-life aging mitigation strategies with example case studies and identifies remaining challenges.

25 ENERGY STORAGE↗
DOE OSTI2023

Predicting electronic structures at any length scale with machine learning

The properties of electrons in matter are of fundamental importance. They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.

36 MATERIALS SCIENCE↗
NASA NTRS2006

Long-Wavelength Infrared (LWIR) Quantum Dot Infrared Photodetector (QDIP) Focal Plane Array

We have exploited the artificial atomlike properties of epitaxially self-assembled quantum dots for the development of high operating temperature long wavelength infrared (LWIR) focal plane arrays. Quantum dots are nanometer-scale islands that form spontaneously on a semiconductor substrate due to lattice mismatch. QDIPs are expected to outperform quantum well infrared detectors (QWIPs) and are expected to offer significant advantages over II-VI material based focal plane arrays. QDIPs are fabricated using robust wide bandgap III-V materials which are well suited to the production of highly uniform LWIR arrays. We have used molecular beam epitaxy (MBE) technology to grow multi-layer LWIR quantum dot structures based on the InAs/InGaAs/GaAs material system. JPL is building on its significant QWIP experience and is basically building a Dot-in-the-Well (DWELL) device design by embedding InAs quantum dots in a QWIP structure. This hybrid quantum dot/quantum well device offers additional control in wavelength tuning via control of dot-size and/or quantum well sizes. In addition the quantum wells can trap electrons and aide in ground state refilling. Recent measurements have shown a 10 times higher photoconductive gain than the typical QWIP device, which indirectly confirms the lower relaxation rate of excited electrons (photon bottleneck) in QDPs. Subsequent material and device improvements have demonstrated an absorption quantum efficiency (QE) of approx. 3%. Dot-in-the-well (DWELL) QDIPs were also experimentally shown to absorb both 45 deg. and normally incident light. Thus we have employed a reflection grating structure to further enhance the quantum efficiency. JPL has demonstrated wavelength control by progressively growing material and fabricating devices structures that have continuously increased in LWIR response. The most recent devices exhibit peak responsivity out to 8.1 microns. Peak detectivity of the 8.1 micrometer devices has reached approx. 1 x 10(exp 10) Jones at 77 K. Furthermore, we have fabricated the first long-wavelength 640x512 pixels QDP focal plane array. This QDIP focal plane may has produced excellent infrared imagery with noise equivalent temperature difference of 40 mK at 60K operating temperature. In addition, we have managed to increase the quantum efficiency of these devices from 0.1% (according to the data published in literature) to 20% in discrete devices. This is a factor of 200 increase in quantum efficiency. With these excellent results, for the first time QDIP performance has surpassed the QWIP performance. Our goal is to operate these long-wavelength detectors at much higher operating temperature than 77K which can be passively achieved in space. This will be a huge leap in high performance infrared detectors specifically applicable to space science instruments.

infrared imaging↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2022

Debottlenecking 4-hydroxybenzoate hydroxylation in Pseudomonas putida KT2440 improves muconate productivity from p -coumarate

The transformation of 4-hydroxybenzoate (4-HBA) to protocatechuate (PCA) is catalyzed by flavoprotein oxygenases known as para-hydroxybenzoate-3-hydroxylases (PHBHs). In Pseudomonas putida KT2440 (P. putida) strains engineered to convert lignin-related aromatic compounds to muconic acid (MA), PHBH activity is rate-limiting, as indicated by the accumulation of 4-HBA, which ultimately limits MA productivity. Here, we hypothesized that replacement of PobA, the native P. putida PHBH, with PraI, a PHBH from Paenibacillus sp. JJ-1b with a broader nicotinamide cofactor preference, could alleviate this bottleneck. Biochemical assays confirmed the strict preference of NADPH for PobA, while PraI can utilize either NADH or NADPH. Kinetic assays demonstrated that both PobA and PraI can utilize NADPH with comparable catalytic efficiency and that PraI also efficiently utilizes NADH at roughly half the catalytic efficiency. The X-ray crystal structure of PraI was solved and revealed absolute conservation of the active site architecture to other PHBH structures despite their differing cofactor preferences. To understand the effect in vivo, we compared three P. putida strains engineered to produce MA from p-coumarate (pCA), showing that expression of praI leads to lower 4-HBA accumulation and decreased NADP+/NADPH ratios relative to strains harboring pobA, indicative of a relieved 4-HBA bottleneck due to increased NADPH availability. In bioreactor cultivations, a strain exclusively expressing praI achieved a titer of 40 g/L MA at 100% molar yield and a productivity of 0.5 g/L/h. Altogether, this study demonstrates the benefit of sampling readily available natural enzyme diversity for debottlenecking metabolic flux in an engineered strain for microbial conversion of lignin-derived compounds to value-added products.

09 BIOMASS FUELS↗
DOE OSTI2022

Accurate parameterization of the kinetic energy functional for calculations using exact-exchange

In this report, electronic structure calculations based on Kohn-Sham density functional theory (KSDFT) that incorporate exact exchange or hybrid functionals are associated with large computational expense, a consequence of the inherent cubic scaling bottleneck and large associated prefactor, which limits the length and time scales that can be accessed. Though orbital-free density functional theory (OFDFT) calculations scale linearly with system size and are associated with significantly smaller prefactor, they are limited by the absence of accurate density-dependent kinetic energy functionals. Therefore, the development of accurate density-dependent kinetic energy functionals is important for OFDFT calculations of large realistic systems. To this end, we propose a method to train kinetic energy functional models at the exact-exchange level of theory by using a dictionary of physically relevant terms that have been proposed in the literature in conjunction with linear or nonlinear regression methods to obtain the fitting coefficients. For our dictionary, we use gradient expansion of the kinetic energy, nonlocal models proposed in the literature and their nonlinear combinations, such as a model that incorporates spatial correlations between higher order derivatives of electron density at two points. The predictive capabilities of these models are assessed by using a variety of model one-dimensional systems that exhibit diverse bonding characteristics, such as a chain of eight hydrogens, LiF, LiH, C 4 H 2 , C 4 N 2 and C 3 O 2 . We show that by using data from model one-dimensional KSDFT calculations performed using the exact-exchange functional for only a few neutral structures, it is possible to generate models with high accuracy for charged systems as well as electron and kinetic energy densities during self-consistent field iterations. In addition, we show that it possible to learn both the orbital dependent terms, i.e., the kinetic energy and the exact-exchange energy, and models that incorporate additional nonlinearities in spatial correlations, such as a quadratic model, are needed to capture subtle features of the kinetic energy density that are present in exact-exchange-based KSDFT calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2015

No-Oven, No-Autoclave Composite Processing

Very large composite structures, such as those used in NASA's Space Launch System, push the boundaries imposed by current autoclaves. New technology is needed to maintain composite performance and free manufacturing engineers from the restraints of curing equipment size limitations. Recent efforts on a Phase II project by Cornerstone Research Group, Inc. (CRG), have advanced the technology and manufacturing readiness levels of a unique two-part epoxy resin system. Designed for room-temperature infusion of a dry carbon preform, the system includes a no-heat-added cure that delivers 350 F composite performance in a matter of hours. This no-oven, no-autoclave (NONA) composite processing eliminates part-size constraints imposed by infrastructure and lowers costs by increasing throughput and reducing capital-specific, process-flow bottlenecks. As a result of the Phase II activity, NONA materials and processes were used to make high-temperature composite tooling suitable for further production of carbon-epoxy laminates and honeycomb/ sandwich-structure composites with an aluminum core. The technology platform involves tooling design, resin infusion processing, composite part design, and resin chemistry. The various technology elements are combined to achieve a fully cured part. The individual elements are not unusual, but they are combined in such a way that enables proper management of the heat generated by the epoxy resin during cure. The result is a self-cured carbon/ epoxy composite part that is mechanically and chemically stable at temperatures up to 350 F. As a result of the successful SBIR effort, CRG has launched NONA Composites as a spinoff subsidiary. The company sells resin to end users, fabricates finished goods for customers, and sells composite tooling made with NONA materials and processes to composite manufacturers.

Rauscher, Michael D.↗
DOE OSTI2022

Multinode Multi-GPU Two-Electron Integrals: Code Generation Using the Regent Language

The computation of two-electron repulsion integrals (ERIs) is often the most expensive step of integral-direct self-consistent field methods. Formally it scales as O(N 4 ), where N is the number of Gaussian basis functions used to represent the molecular wave function. In practice, this scaling can be reduced to O(N 2 ) or less by neglecting small integrals with screening methods. The contributions of the ERIs to the Fock matrix are of Coulomb (J) and exchange (K) type and require separate algorithms to compute matrix elements efficiently. We previously implemented highly efficient GPU-accelerated J-matrix and K-matrix algorithms in the electronic structure code TeraChem. Although these implementations supported the use of multiple GPUs on a node, they did not support the use of multiple nodes. This presents a key bottleneck to cutting-edge ab initio simulations of large systems, e.g., excited state dynamics of photoactive proteins. We present our implementation of multinode multi-GPU J- and K-matrix algorithms in TeraChem using the Regent programming language. Regent directly supports distributed computation in a task-based model and can generate code for a variety of architectures, including NVIDIA GPUs. We demonstrate multinode scaling up to 45 GPUs (3 nodes) and benchmark against hand-coded TeraChem integral code. Finally, we also outline our metaprogrammed Regent implementation, which enables flexible code generation for integrals of different angular momenta.

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

Real-time 3D analysis during electron tomography using tomviz

The demand for high-throughput electron tomography is rapidly increasing in biological and material sciences. However, this 3D imaging technique is computationally bottlenecked by alignment and reconstruction which runs from hours to days. We demonstrate real-time tomography with dynamic 3D tomographic visualization to enable rapid interpretation of specimen structure immediately as data is collected on an electron microscope. Using geometrically complex chiral nanoparticles, we show volumetric interpretation can begin in less than 10 minutes and a high-quality tomogram is available within 30 minutes. Real-time tomography is integrated into tomviz, an open-source and cross-platform 3D data analysis tool that contains intuitive graphical user interfaces (GUI), to enable any scientist to characterize biological and material structure in 3D.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Psychological tools for knowledge acquisition

Knowledge acquisition is said to be the biggest bottleneck in the development of expert systems. The problem is getting the knowledge out of the expert's head and into a computer. In cognitive psychology, characterizing metal structures and why experts are good at what they do is an important research area. Is there some way that the tools that psychologists have developed to uncover mental structure can be used to benefit knowledge engineers? We think that the way to find out is to browse through the psychologist's toolbox to see what there is in it that might be of use to knowledge engineers. Expert system developers have relied on two standard methods for extracting knowledge from the expert: (1) the knowledge engineer engages in an intense bout of interviews with the expert or experts, or (2) the knowledge engineer becomes an expert himself, relying on introspection to uncover the basis of his own expertise. Unfortunately, these techniques have the difficulty that often the expert himself isn't consciously aware of the basis of his expertise. If the expert himself isn't conscious of how he solves problems, introspection is useless. Cognitive psychology has faced similar problems for many years and has developed exploratory methods that can be used to discover cognitive structure from simple data.

Rueter, Henry H.↗