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Results for “Structural error”

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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Error-mitigated nonorthogonal quantum eigensolver via shadow tomography

We present a shadow-tomography-enhanced nonorthogonal quantum eigensolver (NOQE) for more efficient and accurate electronic structure calculations on near-term quantum devices. By integrating shadow tomography into the NOQE, the measurement cost scales linearly rather than quadratically with the number of reference states, while also reducing the required qubits and circuit depth by half. This approach enables extraction of all matrix elements via randomized measurements and classical postprocessing. We analyze its sample complexity and show that, for small systems, it remains constant in the high-precision regime, while for larger systems, it scales linearly with the system size. We further apply shadow-based error mitigation to suppress noise-induced bias without increasing quantum resources. Demonstrations on the hydrogen molecule in the strongly correlated regime achieve chemical accuracy under realistic noise, showing that our method is both resource-efficient and noise-resilient for practical quantum chemistry simulations in the near term.

quantum algorithms & computation↗
DOE OSTI2016

Registration of human skull computed tomography data to an ultrasound treatment space using a sparse high frequency ultrasound hemispherical array

Purpose: Transcranial focused ultrasound (FUS) shows great promise for a range of therapeutic applications in the brain. Current clinical investigations rely on the use of magnetic resonance imaging (MRI) to monitor treatments and for the registration of preoperative computed tomography (CT)-data to the MR images at the time of treatment to correct the sound aberrations caused by the skull. For some applications, MRI is not an appropriate choice for therapy monitoring and its cost may limit the accessibility of these treatments. An alternative approach, using high frequency ultrasound measurements to localize the skull surface and register CT data to the ultrasound treatment space, for the purposes of skull-related phase aberration correction and treatment targeting, has been developed. Methods: A prototype high frequency, hemispherical sparse array was fabricated. Pulse-echo measurements of the surface of five ex vivo human skulls were made, and the CT datasets of each skull were obtained. The acoustic data were used to rigidly register the CT-derived skull surface to the treatment space. The ultrasound-based registrations of the CT datasets were compared to the gold-standard landmark-based registrations. Results: The results show on an average sub-millimeter (0.9 ± 0.2 mm) displacement and subdegree (0.8° ± 0.4°) rotation registration errors. Numerical simulations predict that registration errors on this scale will result in a mean targeting error of 1.0 ± 0.2 mm and reduction in focal pressure of 1.0% ± 0.6% when targeting a midbrain structure (e.g., hippocampus) using a commercially available low-frequency brain prototype device (InSightec, 230 kHz brain system). Conclusions: If combined with ultrasound-based treatment monitoring techniques, this registration method could allow for the development of a low-cost transcranial FUS treatment platform to make this technology more widely available.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-JeP1-05: Dose Deformation Error Associated with Deformable Image Registration Pathways

Purpose: To evaluate errors associated with using different deformable image registration (DIR) pathways to deform dose from planning CT (pCT) to cone-beam CT (CBCT). Methods: Deforming dose is controversial because of the lack of quality assurance tools. We previously proposed a novel metric to evaluate dose deformation error (DDE) by warping dose information using two methods, via dose and contour deformation. First, isodose lines of the pCT were converted into structures and then deformed to the CBCT using an image based deformation map (dose/structure/deform). Alternatively, the dose matrix from the pCT was deformed to CBCT using the same deformation map, and then the same isodose lines of the deformed dose were converted into structures (dose/deform/structure). The doses corresponding to each structure were queried from the deformed dose and full-width-half-maximums were used to evaluate the dose dispersion. The difference between the FWHM of each isodose level structure is defined as the DDE. Three head-and-neck cancer patients were identified. For each patient, two DIRs were performed between the pCT and CBCT, either deforming pCT-to-CBCT or CBCT-to-pCT. We evaluated the errors associated by using either of these pathways to deform dose. A commercially available, Demons based DIR was used for this study, and 10 isodose levels (20% to 105%) were used to evaluate the errors in various dose levels. Results: The prescription dose for all patients was 70 Gy. The mean DDE for CT-to-CBCT deformation was 1.0 Gy (range: 0.3–2.0 Gy) and this was increased to 4.3 Gy (range: 1.5–6.4 Gy) for CBCT-to-CT deformation. The mean increase in DDE between the two deformations was 3.3 Gy (range: 1.0–5.4 Gy). Conclusion: The proposed DDF was used to quantitatively estimate dose deformation errors caused by different pathways to perform DIR. Deforming dose using CBCT-to-CT deformation produced greater error than CT-to-CBCT deformation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference

Here, we report a Bayesian framework for concurrent selection of physics-based models and (modeling) error models. We investigate the use of colored noise to capture the mismatch between the predictions of calibrated models and observational data that cannot be explained by measurement error alone within the context of Bayesian estimation for stochastic ordinary differential equations. Proposed models are characterized by the average data-fit, a measure of how well a model fits the measurements, and the model complexity measured using the Kullback–Leibler divergence. The use of a more complex error models increases the average data-fit but also increases the complexity of the combined model, possibly over-fitting the data. Bayesian model selection is used to find the optimal physical model as well as the optimal error model. The optimal model is defined using the evidence, where the average data-fit is balanced by the complexity of the model. The effect of colored noise process is illustrated using a nonlinear aeroelastic oscillator representing a rigid NACA0012 airfoil undergoing limit cycle oscillations due to complex fluid–structure interactions. Several quasi-steady and unsteady aerodynamic models are proposed with colored noise or white noise for the model error. The use of colored noise improves the predictive capabilities of simpler models.

42 ENGINEERING↗
DOE OSTI2016

A TRACER METHOD FOR COMPUTING TYPE IA SUPERNOVA YIELDS: BURNING MODEL CALIBRATION, RECONSTRUCTION OF THICKENED FLAMES, AND VERIFICATION FOR PLANAR DETONATIONS

We refine our previously introduced parameterized model for explosive carbon–oxygen fusion during thermonuclear Type Ia supernovae (SNe Ia) by adding corrections to post-processing of recorded Lagrangian fluid-element histories to obtain more accurate isotopic yields. Deflagration and detonation products are verified for propagation in a medium of uniform density. A new method is introduced for reconstructing the temperature–density history within the artificially thick model deflagration front. We obtain better than 5% consistency between the electron capture computed by the burning model and yields from post-processing. For detonations, we compare to a benchmark calculation of the structure of driven steady-state planar detonations performed with a large nuclear reaction network and error-controlled integration. We verify that, for steady-state planar detonations down to a density of 5 × 10{sup 6} g cm{sup −3}, our post-processing matches the major abundances in the benchmark solution typically to better than 10% for times greater than 0.01 s after the passage of the shock front. As a test case to demonstrate the method, presented here with post-processing for the first time, we perform a two-dimensional simulation of a SN Ia in the scenario of a Chandrasekhar-mass deflagration–detonation transition (DDT). We find that reconstruction of deflagration tracks leads to slightly more complete silicon burning than without reconstruction. The resulting abundance structure of the ejecta is consistent with inferences from spectroscopic studies of observed SNe Ia. We confirm the absence of a central region of stable Fe-group material for the multi-dimensional DDT scenario. Detailed isotopic yields are tabulated and change only modestly when using deflagration reconstruction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel

Nowcasts, or near-real-time (NRT) forecasts, of soil moisture based on the Soil Moisture Active and Passive (SMAP) mission could provide substantial value for a range of applications including hazards monitoring and agricultural planning. To provide such a NRT forecast with high fidelity, we enhanced a time series deep learning architecture, long short-term memory (LSTM), with a novel data integration (DI) kernel to assimilate the most recent SMAP observations as soon as they become available. The kernel is adaptive in that it can accommodate irregular observational schedules. Testing over the CONUS, this NRT forecast product showcases predictions with unprecedented accuracy when evaluated against subsequent SMAP retrievals. It showed smaller error than NRT forecasts reported in the literature, especially at longer forecast latency. The comparative advantage was due to LSTM’s structural improvements, as well as its ability to utilize more input variables and more training data. The DI-LSTM was compared to the original LSTM model that runs without data integration, referred to as the projection model here. We found that the DI procedure removed the autocorrelated effects of forcing errors and errors due to processes not represented in the inputs, for example, irrigation and floodplain/lake inundation, as well as mismatches due to unseen forcing conditions. The effects of this purely data-driven DI kernel are discussed for the first time in the geosciences. Furthermore, this work presents an upper-bound estimate for the random component of the SMAP retrieval error.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

TU-D-201-06: HDR Plan Prechecks Using Eclipse Scripting API

Purpose: Automate brachytherapy treatment plan quality check using Eclipse v13.6 scripting API based on pre-configured rules to minimize human error and maximize efficiency. Methods: The HDR Precheck system is developed based on a rules-driven approach using Eclipse scripting API. This system checks for critical plan parameters like channel length, first source position, source step size and channel mapping. The planned treatment time is verified independently based on analytical methods. For interstitial or SAVI APBI treatment plans, a Patterson-Parker system calculation is performed to verify the planned treatment time. For endobronchial treatments, an analytical formula from TG-59 is used. Acceptable tolerances were defined based on clinical experiences in our department. The system was designed to show PASS/FAIL status levels. Additional information, if necessary, is indicated appropriately in a separate comments field in the user interface. Results: The HDR Precheck system has been developed and tested to verify the treatment plan parameters that are routinely checked by the clinical physicist. The report also serves as a reminder or checklist for the planner to perform any additional critical checks such as applicator digitization or scenarios where the channel mapping was intentionally changed. It is expected to reduce the current manual plan check time from 15 minutes to <1 minute. Conclusion: Automating brachytherapy plan prechecks significantly reduces treatment plan precheck time and reduces human errors. When fully developed, this system will be able to perform TG-43 based second check of the treatment planning system’s dose calculation using random points in the target and critical structures. A histogram will be generated along with tabulated mean and standard deviation values for each structure. A knowledge database will also be developed for Brachyvision plans which will then be used for knowledge-based plan quality checks to further reduce treatment planning errors and increase confidence in the planned treatment.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Stability Analysis of Substituted Cobaltocenium [Bis(cyclopentadienyl)cobalt(III)] Employing Chemistry-Informed Neural Networks

Cobaltocenium derivatives are promising components of the anion exchange membranes due to their excellent thermal and alkaline stability under the operating conditions of a fuel cell. Here we present an efficient modeling approach of assessing the chemical stability of substituted cobaltocenium CoCp 2 + based on the computed electronic structure enhanced by machine learning techniques. Within the aqueous environment, the positive charge of the metal cation is balanced by the hydroxide anion through formation of the CoCp 2 + OH¯ complexes, whose dissociation is studied within the implicit solvent employing density functional theory. The data set of about 118 species based on 42 substituent groups characterized by a range of electron- donating (ED) and electron-withdrawing (EW) properties is constructed and analyzed. Given 12 carefully chosen chemistry-informed descriptors of the complexes and relevant fragments, the stability of the complexes is found to strongly correlate with the energies of the highest occupied and lowest unoccupied molecular orbitals, modulated by a switching function of the Hirshfeld charge. The latter is used as a measure of the electron withdrawing-donating character of the substituents. Based on this observation from the conventional regression analysis, two fully connected, feed-forward neural network (FNN) models with different unit structures, called the chemistry-informed (CINN) and the quadratic (QNN) neural networks, are developed. Both models predict the bond dissociation energies of the cobaltocenium complexes with mean relative errors less than 5.40% and average absolute errors less than 0.94 kcal/mol. The results show the potential of QNN to efficiently capture more complex relationships. Here, the concept of incorporating the domain (chemical) knowledge/insight into the neural network structure paves the way to applications of machine learning techniques with small data sets, ultimately leading to better predictive models compared to the conventional regression analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning for accuracy in density functional approximations

Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. In this paper, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

FDTD modelling of nanostructured OLEDs: analysis of simulation parameters for accurate radiation patterns

Nanostructures in OLEDs allow to manipulate the radiation pattern and direct light of a specific wavelength into a specific angle in the far field by resonant light outcoupling. We present a theoretical study investigating simulation conditions for accurate radiation patterns employing the FDTD method. Radiation patterns of single dipole emitters placed in a sufficiently large simulation domain are superimposed to model the incoherent emission of a continuous emission layer. The necessary simulation domain size is derived by considering propagation length and field profile of the guided mode of a corresponding unstructured OLED. A 5 nm mesh resolution is determined as a good compromise between accuracy and simulation time. The position of an emitter with respect to the grating has a large influence on the radiation pattern. Nevertheless, we demonstrate for nine different OLED structures that the radiation pattern of a homogeneous emission layer is approximated with an amplitude error of less than 5% by superimposing only four equally spaced dipoles at positions ± (1/8,3/8,5/8,7/8)⋅Λ/2, which avoid points of high symmetry.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Linking sintering stresses to nano modification in the microstructure of BaLa4Ti4O15 by transmission electron microscopy

Highlights: • by systematic HRTEM studies we link the microstructural changes in BLT ceramics to the external pressure applied during sintering; • we developed a mechanistic understanding of the role of stress in sintering; • external stresses during sintering affect the microstructural development at the nanoscale level, being responsible for the creation of higher defective bulk and grain boundary regions; • observation of high density of stacking faults (which generally arrises due to the Ti vacancy layers) indicates that the applied stress is modifying the stacking of AO{sub 3} layers of BLT which in turn creates Ti vacancy layer and stacking faults; • stress assisted sintering has, therefore, the potential to be used as a tool to design microstructures and achieve tailored properties, as suggested from our previous results in BLT and BNT. - Abstract: High quality factor and a temperature stable resonant frequency make BaLa{sub 4}Ti{sub 4}O{sub 15} (BLT) ceramics attractive materials for microwave applications. Aiming to exploit the effects of external stresses on the development of textured and anisotropic microstructures to optimise MW properties, the influence of applied external pressure during sintering of BLT ceramics is analysed. HRTEM and geometric phase analysis (GPA) showed that stresses applied during sintering, trigger the nucleation and growth of faults hypothesised to be due to the errors in the AO{sub 3} layer (basal plane) stacking sequence of the hexagonal perovskite structure. The results reveal a strong correlation between the high concentration of structural defects and the development of anisotropic microstructures, which tune the properties of BLT. Stresses applied during sintering are therefore a promising tool to design material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks

The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Recently, machine learning has been applied to NMR in the prediction of isotropic chemical shifts from a structure. Current machine learning models, however, often ignore the full chemical shift tensor for the easier-to-predict isotropic chemical shift, effectively ignoring a multitude of structural information available in the NMR chemical shift tensor. Here we use an equivariant graph neural network (GNN) to predict full 29 Si chemical shift tensors in silicate materials. The equivariant GNN model predicts full tensors to a mean absolute error of 1.05 ppm and is able to accurately determine the magnitude, anisotropy, and tensor orientation in a diverse set of silicon oxide local structures. When compared with other models, the equivariant GNN model outperforms the state-of-the-art machine learning models by 53%. The equivariant GNN model also outperforms historic analytical models by 57% for isotropic chemical shift and 91% for anisotropy. The software is available as a simple-to-use open-source repository, allowing similar models to be created and trained with ease.

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

Learning in continuous action space for developing high dimensional potential energy models

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a “window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Separability of Mesh Bias and Parametric Uncertainty for a Full System Thermal Analysis

When making computational simulation predictions of multiphysics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. Here, for this analysis, a verification, validation, and uncertainty quantification (VVUQ) workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification (UQ) simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. In conclusion, based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias is used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes use of computing resources.

42 ENGINEERING↗
DOE OSTI2018

Separability of Mesh Bias and Parametric Uncertainty for a Full System Thermal Analysis

When making computational simulation predictions of multi-physics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis a verification, validation and uncertainty quantification workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias are used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes computing resources.

42 ENGINEERING↗
DOE OSTI2019

Exploring the Random Phase Approximation for Materials and Chemical Physics

In this work we test the performance of the fully nonlocal random phase approximation (RPA) for challenging problems in materials chemistry and physics. In this work we assessed the fully nonlocal random phase approximation (RPA) for the delicate energy differences of interest in materials chemistry and physics. The applications in this proposal are challenges for the simpler approximations of Kohn-Sham density functional theory, which are part of the current “standard model” for quantum chemistry and condensed matter physics. Direct RPA includes the full exact exchange energy and a nonlocal correlation energy from the occupied and unoccupied Kohn-Sham orbitals and orbital energies, with an approximate but universal description of long-range van der Waals attraction. RPA still cannot be a benchmark electronic structure method without a correction for its short-range correlation energy which is too deep. The short-range error of RPA can be corrected by a spatially nonlocal, exchange-only kernel. Such kernels could be a more reliable and “intrinsic” approximation to capture accurate ground-state energy differences than the expensive SOSEX and other beyond-RPA corrections. In this project we revealed the strengths and limitations of some model kernels through some materials problems focusing on structural phase transitions and transition metal chemistry. In addition we demonstrated the accuracy of the Renormalized RPA for total correlation energies of simple solids, and the pressure-induced phase transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Correction to “From Structure to Function: Zn/Mn-Modified Maghemite as an Advanced Nanoplatform for Magnetic Hyperthermia and Radionuclide Therapy”

In the original paper, the acknowledgment contained an error in the attribution of the financial support. The correction is necessary to accurately reflect the source of funding that supported the contributions of the authors from the Institute of Physics Belgrade. The corrected Acknowledgments section is below. Here, this correction pertains solely to the funding acknowledgment and does not affect the scientific content, data, results, or conclusion of the work in any way.

Cancer therapy↗
DOE OSTI2015

Seeding for sirtuins: microseed matrix seeding to obtain crystals of human Sirt3 and Sirt2 suitable for soaking

In the present study, microseed matrix seeding was successfully applied to obtain a large number of crystals of the human sirtuin isotypes Sirt2 and Sirt3. These crystals appeared predictably in diverse crystallization conditions, diffracted to a higher resolution than reported in the literature and were subsequently used to study the protein–ligand interactions of two indole inhibitors. Sirtuins constitute a family of NAD{sup +}-dependent enzymes that catalyse the cleavage of various acyl groups from the ∊-amino group of lysines. They regulate a series of cellular processes and their misregulation has been implicated in various diseases, making sirtuins attractive drug targets. To date, only a few sirtuin modulators have been reported that are suitable for cellular research and their development has been hampered by a lack of structural information. In this work, microseed matrix seeding (MMS) was used to obtain crystals of human Sirt3 in its apo form and of human Sirt2 in complex with ADP ribose (ADPR). Crystal formation using MMS was predictable, less error-prone and yielded a higher number of crystals per drop than using conventional crystallization screening methods. The crystals were used to solve the crystal structures of apo Sirt3 and of Sirt2 in complex with ADPR at an improved resolution, as well as the crystal structures of Sirt2 in complex with ADPR and the indoles EX527 and CHIC35. These Sirt2–ADPR–indole complexes unexpectedly contain two indole molecules and provide novel insights into selective Sirt2 inhibition. The MMS approach for Sirt2 and Sirt3 may be used as the basis for structure-based optimization of Sirt2/3 inhibitors in the future.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗