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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Parallel seed-based approach to multiple protein structure similarities detection

Finding similarities between protein structures is a crucial task in molecular biology. Most of the existing tools require proteins to be aligned in order-preserving way and only find single alignments even when multiple similar regions exist. We propose a new seed-based approach that discovers multiple pairs of similar regions. Its computational complexity is polynomial and it comes with a quality guarantee—the returned alignments have both root mean squared deviations (coordinate-based as well as internal-distances based) lower than a given threshold, if such exist. We do not require the alignments to be order preserving (i.e., we consider nonsequential alignments), which makes our algorithm suitable for detecting similar domains when comparing multidomain proteins as well as to detect structural repetitions within a single protein. Because the search space for nonsequential alignments is much larger than for sequential ones, the computational burden is addressed by extensive use of parallel computing techniques: a coarse-grain level parallelism making use of available CPU cores for computation and a fine-grain level parallelism exploiting bit-level concurrency as well as vector instructions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

SU-E-T-540: MCNPX Simulation of Proton Dose Distributions in a Water Phantom

Purpose: In this study, fluence and energy deposition of proton and proton by-products and dose distributions were simulated. Lateral dose distributions were also been discussed to understand the difference between Monte Carlo simulations and pencil beam algorithm. Methods: MCNPX codes were used to build a water phantom by using “repeated structures” technique and the doses and fluences in each cell was recorded by mesh tally. This study includes, proton equilibrium and proton disequilibrium case. For the proton equilibrium case, the doses difference between proton and proton by-products were studied. A 160 MeV proton pencil beam was perpendicularly incident into a 40 × 40 × 50 cm{sup 3} water phantom and the scoring volume was 20 × 20 × 0.2 cm{sup 3}. Energy deposition and fluence were calculated from MCNPX with (1) proton only; and (2) proton and secondary particles. For the proton disequilibrium case, the dose distribution variation using different multiple Coulomb scattering were studied. A 70 MeV proton pencil beam was perpendicularly incident into a 40 × 40 × 10 cm{sup 3} water phantom and two scoring voxel sizes of 0.1 × 0.1 × 0.05 cm{sup 3} and 0.01 × 0.01 × 0.05 cm{sup 3} were used for the depth dose distribution, and 0.01 × 0.01 × 0.05 cm{sup 3} for the lateral profile distribution simulations. Results: In the water phantom, proton fluence and dose in depths beyond the Bragg peak were slightly perturbed by the choice of the simulated particle types. The dose from secondary particles was about three orders smaller, but its simulation consumed significant computing time. The depth dose distributions and lateral dose distributions of 70 MeV proton pencil beam obtained from MCNPX, GEANT4, and the pencil beam algorithm showed the significant deviations, probably caused by multiple Coulomb scattering. Conclusion: Multiple Coulomb scattering is critical when there is in proton disequilibrium.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

Enhancing pairwise state-transition weights: A new weighting scheme in simulated tempering that can minimize transition time between a pair of conformational states

Simulated tempering (ST) is a widely used enhancing sampling method for Molecular Dynamics simulations. As one expanded ensemble method, ST is a combination of canonical ensembles at different temperatures and the acceptance probability of cross-temperature transitions is determined by both the temperature difference and the weights of each temperature. One popular way to obtain the weights is to adopt the free energy of each canonical ensemble, which achieves uniform sampling among temperature space. However, this uniform distribution in temperature space may not be optimal since high temperatures do not always speed up the conformational transitions of interest, as anti-Arrhenius kinetics are prevalent in protein and RNA folding. Here, we propose a new method: Enhancing Pairwise State-transition Weights (EPSW), to obtain the optimal weights by minimizing the round-trip time for transitions among different metastable states at the temperature of interest in ST. The novelty of the EPSW algorithm lies in explicitly considering the kinetics of conformation transitions when optimizing the weights of different temperatures. We further demonstrate the power of EPSW in three different systems: a simple two-temperature model, a two-dimensional model for protein folding with anti-Arrhenius kinetics, and the alanine dipeptide. The results from these three systems showed that the new algorithm can substantially accelerate the transitions between conformational states of interest in the ST expanded ensemble and further facilitate the convergence of thermodynamics compared to the widely used free energy weights. We anticipate that this algorithm is particularly useful for studying functional conformational changes of biological systems where the initial and final states are often known from structural biology experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Clustering-based biased Monte Carlo Approach to Protein Titration Curve Prediction

We develop and implement a novel approach to computing the ensemble averages in systems characterized by pair-wise interactions between the entities. Methods involving full enumeration of the configuration space result in exponential complexity. Sampling methods such as Markov Chain Monte Carlo (MCMC) algorithms have been proposed to tackle the exponential complexity of these problems. In certain scenarios where significant energetic coupling exists between the entities, the accuracy of the such algorithms can be diminished. We propose a strategy to improve the accuracy of the MCMC runs by taking advantage of the cluster structure in the interaction energy matrix. We propose two different schemes for performing the biased MCMC runs on the partitioned systems and show that they are valid MCMC schemes. We then apply these algorithms to the problem of computing the protonation fractions and hence the titration curves of titratable protein residues that constitute a given protein. We leverage both synthesized and real-world systems and show the improved performance of our biased MCMC methods when compared to the regular MCMC method.

Visweswara Sathanur, Arun↗
DOE OSTI2015

The Particle Accelerator Simulation Code PyORBIT

The particle accelerator simulation code PyORBIT is presented. The structure, implementation, history, parallel and simulation capabilities, and future development of the code are discussed. The PyORBIT code is a new implementation and extension of algorithms of the original ORBIT code that was developed for the Spallation Neutron Source accelerator at the Oak Ridge National Laboratory. The PyORBIT code has a two level structure. The upper level uses the Python programming language to control the flow of intensive calculations performed by the lower level code implemented in the C++ language. The parallel capabilities are based on MPI communications. The PyORBIT is an open source code accessible to the public through the Google Open Source Projects Hosting service.

Accelerator Simulation↗
DOE OSTI2019

Spatial characterization of heterogeneous nanopore surfaces from XCT scans of Niobrara shale

The multiphase fluid flow through natural and geo-architected nanopore structures is fundamentally controlled by the geometry of such nanopore structures, pore wettability and interfacial tension. Furthermore, multiphase fluid flow through the nanopore structures is compromised when the pore surfaces are heterogeneous, having surface regions of different composition, polarity, and wetting characteristics. In this regard, compositional analysis of the pore channel surface has been a big challenge in the past, since 3D analysis is required. Currently, due to advances in nano X-ray CT, with a voxel resolution of 20 nm, it is possible to identify nanopore network structures and quantify their surface composition in 3D. In this paper, the development of procedures for detailed analysis to quantify the composition of the pore channel surface regions, including the corresponding area, is described using nanoCT images. Image analysis procedures, including preprocessing of the 3D tomographic images, correction for the partial volume effect (PVE), and the use of a new algorithm for determination of pore channel surface composition and area are reported on. Videos of the nanopore structure have been provided as supplementary information to further describe the heterogeneity of the nanopore surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Neural architecture search via similarity adaptive guidance

Evolutionary neural network architecture search (ENAS) has attracted the attention of many experts due to its global optimization capabilities to automatically search for convolutional neural network architectures based on the target task. The current search space for ENAS is not to design a fully structured network, but to search for smaller cell architectures to reduce search costs. However, blind search strategies do not effectively utilize the potential experience of the population. In order to utilize the potential experience learned by the current population to guide the evolutionary search of the population, we propose a similarity guided neural network architecture search algorithm based on cell architecture, which utilizes the similarity between pairwise architectures in the population as empirical knowledge learned by the population. Our proposed algorithm provides a novel method for calculating architecture similarity, which calculates architecture similarity separately from the cell and macro-structure. Then we decouple the connections and operations in the cell and calculate connection and operation similarity separately. In addition, we propose adaptive similarity selection and binary tournament selection strategies to enhance the algorithm’s global and local search capabilities and effectively explore the search space. Finally, we design an improved single-point crossover operator to enhance the local search ability of the evolutionary operator. The experimental results show that SAGNAS is a competitive algorithm that achieves 97.44% and 81.60% in CIFAR10 and CIFAR100 with only 1.9 GPU-days spent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Heterogeneous techniques for rescaling energy deposits in the CMS Phase-2 endcap calorimeter

We present the porting to heterogeneous architectures of the algorithm used for applying linear transformations of raw energy deposits in the CMS High Granularity Calorimeter (HGCAL). This is the first heterogeneous algorithm to be fully integrated with HGCAL’s reconstruction chain. After introducing the latter and giving a brief description of the structural components of HGCAL relevant for this work, the role of the linear transformations in the calibration is reviewed. The many ways in which parallelization is achieved are described, and the successful validation of the heterogeneous algorithm is covered. Detailed performance measurements are presented, including throughput and execution time for both CPU and GPU algorithms, therefore establishing the corresponding speedup. We finally discuss the interplay between this work and the porting of other algorithms in the existing reconstruction chain, as well as integrating algorithms previously ported but not yet integrated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Structures and stability of metal-doped Ge n M (n = 9, 10) clusters

The lowest-energy structures of neutral and cationic Ge n M (n = 9, 10; M = Si, Li, Mg, Al, Fe, Mn, Pb, Au, Ag, Yb, Pm and Dy) clusters were studied by genetic algorithm (GA) and first-principles calculations. The calculation results show that doping of the metal atoms and Si into Ge 9 and Ge 10 clusters is energetically favorable. Most of the metal-doped Ge cluster structures can be viewed as adding or substituting metal atom on the surface of the corresponding ground-state Ge n clusters. However, the neutral and cationic FeGe 9,10 ,MnGe 9,10 and Ge 10 Al are cage-like with the metal atom encapsulated inside. Such cage-like transition metal doped Ge n clusters are shown to have higher adsorption energy and thermal stability. Our calculation results suggest that Ge 9,10 Fe and Ge 9 Si would be used as building blocks in cluster-assembled nanomaterials because of their high stabilities.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2014

Towards a unified description of the hydrogen bond network of liquid water: A dynamics based approach

The definition of a hydrogen bond (H-bond) is intimately related to the topological and dynamic properties of the hydrogen bond network within liquid water. The development of a universal H-bond definition for water is an active area of research as it would remove many ambiguities in the network properties that derive from the fixed definition employed to assign whether a water dimer is hydrogen bonded. This work investigates the impact that an electronic-structure based definition, an energetic, and a geometric definition of the H-bond has upon both topological and dynamic network behavior of simulated water. In each definition, the use of a cutoff (either geometric or energetic) to assign the presence of a H-bond leads to the formation of transiently bonded or broken dimers, which have been quantified within the simulation data. The relative concentration of transient species, and their duration, results in two of the three definitions sharing similarities in either topological or dynamic features (H-bond distribution, H-bond lifetime, etc.), however no two definitions exhibit similar behavior for both classes of network properties. In fact, two networks with similar local network topology (as indicated by similar average H-bonds) can have dramatically different global network topology (as indicated by the defect state distributions) and altered H-bond lifetimes. A dynamics based correction scheme is then used to remove artificially transient H-bonds and to repair artificially broken bonds within the network such that the corrected network exhibits the same structural and dynamic properties for two H-bond definitions (the properties of the third definition being significantly improved). The algorithm described represents a significant step forward in the development of a unified hydrogen bond network whose properties are independent of the original hydrogen bond definition that is employed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2021

Removal of spurious data in Bragg coherent diffraction imaging: an algorithm for automated data preprocessing

Bragg coherent diffraction imaging (BCDI) provides a powerful tool for obtaining high-resolution structural information from nanocrystalline materials. Here a BCDI sample consisting of a large number of randomly oriented nanoscale crystals is considered. Ideally, only one crystal is oriented to produce a Bragg peak on the detector. However, diffraction from other crystals often produces additional signals on the detector. Before the measured diffraction patterns can be processed into structural images, scientists routinely need to manually identify and remove the `alien' intensities from sources other than the intended crystal. With the development of modern high-coherence storage rings, such as the upgraded Advanced Photon Source (APS), the already slow process of manual preprocessing will be untenable for the large volumes of data that will be produced. An automated method of identifying and deleting alien intensities is proposed. This method exploits the fact that BCDI of a perfect crystal produces diffraction data with inversion symmetry around the Bragg peak. This approach uses the machine learning clustering method DBSCAN to distinguish between diffraction from multiple sources, and then calculates cluster size and inversion symmetry to assess whether clusters of intensity belong to desired data or alien signals. This approach can dramatically reduce the amount of time spent manually processing data, allowing BCDI data processing capabilities to keep pace with the technological advances of fourth-generation synchrotron light sources.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-T-513: Investigating Dose of Internal Target Volume After Correcting for Tissue Heterogeneity in SBRT Lung Plans with Homogeneity Calculation

Purpose It was recommended to use the prescription of 54 Gy/3 with heterogeneity corrections for previously established dose scheme of 60 Gy/3 with homogeneity calculation. This study is to investigate dose coverage for the internal target volume (ITV) with and without heterogeneity correction. Methods Thirty patients who received stereotactic body radiotherapy (SBRT) to a dose of 60 Gy in 3 fractions with homogeneous planning for early stage non-small-cell lung cancer (NSCLC) were selected. ITV was created either from 4DCT scans or a fusion of multi-phase respiratory scans. Planning target volume (PTV) was a 5 mm expansion of the ITV. For this study, we recalculated homogeneous clinical plans using heterogeneity corrections with monitor units set as clinically delivered. All plans were calculated with 3 mm dose grids and collapsed cone convolution algorithm. To account for uncertainties from tumor delineation and image-guided radiotherapy, a structure ITV2mm was created by expanding ITV with 2 mm margins. Dose coverage to the PTV, ITV and ITV2mm were compared with a student paired t-test. Results With heterogeneity corrections, the PTV V60Gy decreased by 10.1% ± 18.4% (p<0.01) while the maximum dose to the PTV increased by 3.7 ± 4.3% (p<0.01). With and without corrections, D99% was 65.8 ± 4.0 Gy and 66.7 ± 4.8 Gy (p=0.15) for the ITV, and 63.9 ± 3.4 Gy and 62.9 ± 4.6 Gy for the ITV2mm (p=0.22), respectively. The mean dose to the ITV and ITV2mm increased 3.6% ± 4.7% (p<0.01) and 2.3% ± 5.2% (p=0.01) with heterogeneity corrections. Conclusion After heterogeneity correction, the peripheral coverage of the PTV decreased to approximately 54 Gy, but D99% of the ITV and ITV2mm was unchanged and the mean dose to the ITV and ITV2mm was increased. Clinical implication of these results requires more investigation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Machine learnt bond order potential to model metal–organic (Co–C) heterostructures

A fundamental understanding of the inter-relationships between structure, morphology, atomic scale dynamics, chemistry, and physical properties of mixed metallic-covalent systems is essential to design novel functional materials for applications in flexible nano-electronics, energy storage and catalysis. To achieve such knowledge, it is imperative to develop robust and computationally efficient atomistic models that describe atomic interactions accurately within a single framework. Here, we present a unified Tersoff–Brenner type bond order potential (BOP) for a Co–C system, trained against lattice parameters, cohesive energies, equation of state, and elastic constants of different crystalline phases of cobalt as well as orthorhombic Co2C derived from density functional theory (DFT) calculations. The independent BOP parameters are determined using a combination of supervised machine learning (genetic algorithms) and local minimization via the simplex method. Our newly developed BOP accurately describes the structural, thermodynamic, mechanical, and surface properties of both the elemental components as well as the carbide phases, in excellent accordance with DFT calculations and experiments. Using our machine-learnt BOP potential, we performed large-scale molecular dynamics simulations to investigate the effect of metal/carbon concentration on the structure and mechanical properties of porous architectures obtained via self-assembly of cobalt nanoparticles and fullerene molecules. Such porous structures have implications in flexible electronics, where materials with high electrical conductivity and low elastic stiffness are desired. Using unsupervised machine learning (clustering), we identify the pore structure, pore-distribution, and metallic conduction pathways in self-assembled structures at different C/Co ratios. We find that as the C/Co ratio increases, the connectivity between the Co nanoparticles becomes limited, likely resulting in low electrical conductivity; on the other hand, such C-rich hybrid structures are highly flexible (i.e., low stiffness). The BOP model developed in this work is a valuable tool to investigate atomic scale processes, structure–property relationships, and temperature/pressure response of Co–C systems, as well as design organic–inorganic hybrid structures with a desired set of properties.

Narayanan, Badri↗
DOE OSTI2017

Model Order Reduction Algorithm for Estimating the Absorption Spectrum

The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator’s eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comes at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the problem dimension.

IDREAM, K-Edge, theoretical, computational, electr↗
DOE OSTI2019

Efficient Ab Initio Auxiliary-Field Quantum Monte Carlo Calculations in Gaussian Bases via Low-Rank Tensor Decomposition

We describe an algorithm to reduce the cost of auxiliary-field quantum Monte Carlo (AFQMC) calculations for the electronic structure problem. The technique uses a nested low-rank factorization of the electron repulsion integral (ERI). While the cost of conventional AFQMC calculations in Gaussian bases scales as O(N 4 ), where N is the size of the basis, we show that ground-state energies can be computed through tensor decomposition with reduced memory requirements and subquartic scaling. Here, the algorithm is applied to hydrogen chains and square grids, water clusters, and hexagonal BN. In all cases, we observe significant memory savings and, for larger systems, reduced, subquartic simulation time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Grain boundary phases in bcc metals

We report a computational discovery of novel grain boundary structures and multiple grain boundary phases in elemental body-centered cubic (bcc) metals represented by tungsten, tantalum and molybdenum. While grain boundary structures created by the γ-surface method as a union of two perfect half crystals have been studied extensively, it is known that the method has limitations and does not always predict the correct ground states. Here, we use a newly developed computational tool, based on evolutionary algorithms, to perform a grand-canonical search of a high-angle symmetric tilt and twist boundaries in tungsten, and we find new ground states and multiple phases that cannot be described using the conventional structural unit model. We use molecular dynamics (MD) simulations to demonstrate that the new structures can coexist at finite temperature in a closed system, confirming these are examples of different grain boundary phases. The new ground state is confirmed by first-principles calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Optimization of Cooling Water Flow Rate in Nuclear and Thermal Power Plants Based on a Mathematical Model of Cooling Systems{sup 1}

A mathematical model and algorithms are proposed for automatic calculation of the optimum flow rate of cooling water in nuclear and thermal power plants with cooling systems of arbitrary complexity. An unlimited number of configuration and design variants are assumed with the possibility of obtaining a result for any computational time interval, from monthly to hourly. The structural solutions corresponding to an optimum cooling water flow rate can be used for subsequent engineering-economic evaluation of the best cooling system variant. The computerized mathematical model and algorithms make it possible to determine the availability and degree of structural changes for the cooling system in all stages of the life cycle of a plant.

42 ENGINEERING↗