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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Computational Study of Ethanol Conversion on Al 8 O 12 as a Model for γ-Al 2 O 3

Correlated molecular orbital theory at the coupled cluster CCSD(T) level with density functional theory geometries is used to study ethanol dehydration, dehydrogenation, and condensation reactions on an the Al8O12 cluster which is a model for γ-Al2O3. The Al in the active site on the cluster is a strong Lewis acid. The reactions begin with formation of a very stable Lewis acid–base ethanol–cluster adduct. Dehydration proceeds by β-H transfer to a bicoordinate oxygen leading to the direct formation of ethylene and two OH groups following an E2 mechanism. Dehydrogenation proceeds directly by α-H transfer to the active metal center and a proton transfer to a bicoordinate bridge O to form acetaldehyde plus a metal hydride and a hydroxyl, again an E2 mechanism. After addition of a second ethanol, diethyl ether is generated by an α-C transfer from the first to the second ethanol, an acid-driven SN2 mechanism. Condensation and dehydration with two alcohols have comparable energy barriers. The addition of a second ethanol or a water molecule raises the energy barriers. Condensation and dehydration are predicted to be more likely than dehydrogenation. The computational results for the mechanism and the energetics agree well with the available experimental data.

Fang, Zongtang↗
DOE OSTI2024

Clustering and Cliques in Preferential Attachment Random Graphs with Edge Insertion

In this paper, we investigate the global clustering coefficient (a.k.a transitivity) and clique number of graphs generated by a preferential attachment random graph model with an additional feature of allowing edge connections between existing vertices. Specifically, at each time step t, either a new vertex is added with probability f(t), or an edge is added between two existing vertices with probability 1 – f(t). We establish concentration inequalities for the global clustering and clique number of the resulting graphs under the assumption that f(t) is a regularly varying function at infinity with index of regular variation –$\gamma$, where $\gamma$ $\in$ [0, 1). Finally, we also demonstrate an inverse relation between these two statistics: the clique number is essentially the reciprocal of the global clustering coefficient.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Big Data Analytics for Demand Response: Clustering Over Space and Time

The pervasive deployment of advanced sensing infrastructure in Cyber-Physical systems, such as the Smart Grid, has resulted in an unprecedented data explosion. Such data exhibit both large volumes and high velocity characteristics, two of the three pillars of Big Data, and have a time-series notion as datasets in this context typically consist of successive measurements made over a time interval. Time-series data can be valuable for data mining and analytics tasks such as identifying the “right” customers among a diverse population, to target for Demand Response programs. However, time series are challenging to mine due to their high dimensionality. In this paper, we motivate this problem using a real application from the smart grid domain. We explore novel representations of time-series data for BigData analytics, and propose a clustering technique for determining natural segmentation of customers and identification of temporal consumption patterns. Our method is generizable to large-scale, real-world scenarios, without making any assumptions about the data. We evaluate our technique using real datasets from smart meters, totaling ~ 18,200,000 data points, and show the efficacy of our technique in efficiency detecting the number of optimal number of clusters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A space for lattice representation and clustering

Algorithms for quantifying the differences between two lattices are used for Bravais lattice determination, database lookup for unit cells to select candidates for molecular replacement, and recently for clustering to group together images from serial crystallography. It is particularly desirable for the differences between lattices to be computed as a perturbation-stable metric, i.e. as distances that satisfy the triangle inequality, so that standard tree-based nearest-neighbor algorithms can be used, and for which small changes in the lattices involved produce small changes in the distances computed. A perturbation-stable metric space related to the reduction algorithm of Selling and to the Bravais lattice determination methods of Delone is described. Two ways of representing the space, as six-dimensional real vectors or equivalently as three-dimensional complex vectors, are presented and applications of these metrics are discussed. (Note: in his later publications, Boris Delaunay used the Russian version of his surname, Delone.)

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optimizing High Performance Markov Clustering for Pre-Exascale Architectures

HipMCL is a high-performance distributed memory implementation of the popular Markov Cluster Algorithm (MCL) and can cluster large-scale networks within hours using a few thousand CPU-equipped nodes. It relies on sparse matrix computations and heavily makes use of the sparse matrix-sparse matrix multiplication kernel (SpGEMM). The existing parallel algorithms in HipMCL are not scalable to Exascale architectures, both due to their communication costs dominating the runtime at large concurrencies and also due to their inability to take advantage of accelerators that are increasingly popular. In this work, we systematically remove scalability and performance bottlenecks of HipMCL. We enable GPUs by performing the expensive expansion phase of the MCL algorithm on GPU. Additionally, we propose a CPU-GPU joint distributed SpGEMM algorithm called pipelined Sparse SUMMA and integrate a probabilistic memory requirement estimator that is fast and accurate. Furthermore, we develop a new merging algorithm for the incremental processing of partial results produced by the GPUs, which improves the overlap efficiency and the peak memory usage. We also integrate a recent and faster algorithm for performing SpGEMM on CPUs. We validate our new algorithms and optimizations with extensive evaluations. With the enabling of the GPUs and integration of new algorithms, HipMCL is up to 12.4x faster, being able to cluster a network with 70 million proteins and 68 billion connections just under 15 minutes using 1024 nodes of ORNL's Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

WE-DE-202-00: Connecting Radiation Physics with Computational Biology

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-DE-202-01: Connecting Nanoscale Physics to Initial DNA Damage Through Track Structure Simulations

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-DE-202-02: Are Track Structure Simulations Truly Needed for Radiobiology at the Cellular and Tissue Levels?

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-DE-202-03: Modeling of Biological Processes - What Happens After Early Molecular Damage?

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are the most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Massively scalable workflows for quantum chemistry: BigChem and ChemCloud

Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. Here we present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Molybdenum carbide nanoparticles as catalysts for oil sands upgrading: Dynamics and free-energy profiles

There is no doubt that a huge gap exists in understanding heterogeneous catalysis between a cluster model of a few atoms and a bulk model of periodic slabs. Nanoparticles, which are crucial in heterogeneous catalysis in industry, lie in the middle of the gap. We present here our work on the computational modelling of molybdenum carbide nanoparticles (MCNPs) as the catalysts for the upgrading of oil sands in the in-situ environment, using benzene hydrogenation as a model reaction. With a cluster model, efforts were first made to understand the mechanism of the reaction with a density functional theory (DFT) study on the adsorption of benzene and its hydrogenation product – cyclohexane, as well as the cyclic hydrogenation reaction intermediates on the Mo{sub 2}C(0001) surface. From the thermodynamic data, along with literature information, it was found that the benzene hydrogenation reaction on molybdenum carbide happens most likely through a Langmuir-Hinshelwood mechanism with the gradual lifting up of the benzene molecule. The electron localization function (ELF) was then used to help understand the nature of the interactions between the MCNPs, identifying strong multi-center interactions between the adsorbates and the MCNPs. To enable the treatment of larger nanoparticles, a fast semi-empirical density functional tight-binding (DFTB) method was parameterized. With this method, the potential energy profiles of benzene hydrogenation reactions on different sizes of MCNPs are calculated. The study was then extended to consider a MCNP embedded in solvent (benzene), using a quantum mechanical (DFTB) / molecular mechanical approach. Calculations on the free energies profiles with the umbrella sampling method show that the entropy of the MCNPs and the solvent are essential in understanding the catalytic activity of the transition metal related nanoparticles for solid/liquid heterogeneous catalysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

National Computational Infrastructure for Lattice Gauge Theory SciDAC-2 Closeout Report

As part of this project work, researchers from Vanderbilt University, Fermi National Laboratory and Illinois Institute of technology developed a real-time cluster fault-tolerant cluster monitoring framework. This framework is open source and is available for download upon request. This work has also been used at Fermi Laboratory, Vanderbilt University and Mississippi State University across projects other than LQCD. The goal for the scientific workflow project is to investigate and develop domain-specific workflow tools for LQCD to help effectively orchestrate, in parallel, computational campaigns consisting of many loosely-coupled batch processing jobs. Major requirements for an LQCD workflow system include: a system to manage input metadata, e.g. physics parameters such as masses, a system to manage and permit the reuse of templates describing workflows, a system to capture data provenance information, a systems to manage produced data, a means of monitoring workflow progress and status, a means of resuming or extending a stopped workflow, fault tolerance features to enhance the reliability of running workflows. Requirements for an LQCD workflow system are available in documentation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Exploring adverse drug events at the class level

Background: While the association between a drug and an adverse event (ADE) is generally detected at the level of individual drugs, ADEs are often discussed at the class level, i.e., at the level of pharmacologic classes (e.g., in drug labels). We propose two approaches, one visual and one computational, to exploring the contribution of individual drugs to the class signal. Methods: Having established a dataset of ADEs from MEDLINE, we aggregate drugs into ATC classes and ADEs into high-level MeSH terms. We compute statistical associations between drugs and ADEs at the drug level and at the class level. Finally, we visualize the signals at increasing levels of resolution using heat maps. We also automate the exploration of drug-ADE associations at the class level using clustering techniques. Results: Using our visual approach, we were able to uncover known associations, e.g., between fluoroquinolones and tendon injuries, and between statins and rhabdomyolysis. Using our computational approach, we systematically analyzed 488 associations between a drug class and an ADE. Conclusions: The findings gained from our exploratory techniques should be of interest to the curators of ADE repositories and drug safety professionals. Our approach can be applied to different drug-ADE datasets, using different drug classification systems and different signal detection algorithms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

We report that local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. Finally, we also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advances in ArborX to support exascale applications

ArborX is a performance portable geometric search library developed as part of the Exascale Computing Project (ECP). In this paper, we explore a collaboration between ArborX and a cosmological simulation code HACC. Large cosmological simulations on exascale platforms encounter a bottleneck due to the in-situ analysis requirements of halo finding, a problem of identifying dense clusters of dark matter (halos). This problem is solved by using a density-based DBSCAN clustering algorithm. With each MPI rank handling hundreds of millions of particles, it is imperative for the DBSCAN implementation to be efficient. In addition, the requirement to support exascale supercomputers from different vendors necessitates performance portability of the algorithm. We describe how this challenge problem guided ArborX development, and enhanced the performance and the scope of the library. We explore the improvements in the basic algorithms for the underlying search index to improve the performance, and describe several implementations of DBSCAN in ArborX. Further, we report the history of the changes in ArborX and their effect on the time to solve a representative benchmark problem, as well as demonstrate the real world impact on production end-to-end cosmology simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The apparent reversal of the Law of Mass Action in concentrated multicomponent aqueous solutions

Although it is well known that aqueous electrolyte solutions behave non-ideally, few are so non-ideal that adding an electrolyte to a saturated solution with a common ion enhances rather than depresses solubility. Nonetheless, this apparent reversal of the Law of Mass Action (LMA) has been observed in simple nitrate solutions at high concentrations. A leading hypothesis is that ion clusters are formed and stabilized by ions having different charge densities. We report the present study examines this concept, in part by reviewing relevant data from multicomponent aqueous solutions containing sodium nitrate (NaNO 3 ), sodium nitrite (NaNO 2 ), sodium hydroxide (NaOH), and sodium aluminate (NaAl(OH) 4 ) - the major constituents in alkaline nuclear waste. Here, NaOH and NaAl(OH) 4 did not enhance the solubility of NaNO 3 or NaNO 2 , whereas NaNO 2 and NaNO 3 enhanced rather than depressed the solubility of each other despite each having the sodium cation (Na + ) in common. Solutions evaluated in this study have more than 20 molal total Na + concentration, and most have less than one mole of water per mole of ion. Thus, this reversal of the LMA occurs in solutions where there is not enough water to fully hydrate the ions, pointing to the importance of ion cluster formation. Within the composition range of the solutions analyzed here, this reversal of the LMA occurs regardless of NaAl(OH) 4 and NaOH concentrations. Elevated temperatures also result in the reversal of the LMA in the subsystems NaNO 2 -NaNO 3 -H 2 O and NaOH-NaNO 3 -H 2 O, consistent with spectroscopic and computational studies showing enhanced interactions at higher temperatures. Although speciation in these highly concentrated electrolyte solutions is not well understood, the stabilization of ion clusters by: (i) optimizing charge density around the ions through different combinations of mixed cations and anions in solution; and (ii) high temperatures where ions experience prolonged contact, offers important clues for future research into how these species control solubility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗