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DOE OSTI2023

Engineering Supramolecular Hybrid Architectures with Directional Organofluorine Bonds

Understanding how chemical modifications alter the atomic‐scale organization of materials is of fundamental importance in materials engineering and the target of considerable efforts in computational prediction. Incorporating covalent and noncovalent interactions in designing crystals while “piggybacking” on the driving force of molecular self‐assembly has augmented efforts to understand the emergence of complex structures using directed synthesis. In this work, microcrystalline powders of the silver 2‐, 3‐, and 4‐fluorobenzenethiolates are prepared and their structures are resolved by small‐molecule serial femtosecond X‐ray crystallography. These three compounds enable the emergence and role of supramolecular synthons in the crystal structures of 3D metal‐organic chalcogenolates to be examined. The unique divergence in their optoelectronic, morphological, and structural behaviors is assessed. The extent of CHF interactions and their influence on the structure and the observed trends in the thermal stability of the crystals are quantified through theoretical calculations and thermogravimetric analysis.

Kotei, Patience A.↗

DOE OSTI2013

Effects of error covariance structure on estimation of model averaging weights and predictive performance

When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cek resolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

error correlation↗

DOE OSTI2019

Compile-Time Library Call Detection Using CAASCADE and XALT

CAASCADE — Compiler-Assisted Application Source Code Analysis and DatabasE—is a tool that summarizes the use of parallel programming language features in application source code using compiler technology. This paper discusses the library detection capability within CAASCADE to find information about the usage of scientific libraries within the source code. The information that CAASCADE collects provides insights into the usage of library calls in an applications. CAASCADE can classify the APIs by scientific libraries (e.g. LAPACK, BLAS, FFTW, etc). It can also detect the context in which a library API is being invoked, for example within a serial or multi-threaded region. To collect this information, CAASCADE uses compiler plugins that summarize procedural information and uses Apache Spark to do inter-procedural analysis to reconstruct call chains. In addition to this, we also integrated CAASCADE to work with XALT to collect library information based on linkage and modules installed on a system.

Zhao, Jisheng↗

DOE OSTI2024

MatRIS: Addressing the Challenges for Portability and Heterogeneity Using Tasking for Matrix Decomposition (Cholesky)

The ubiquitous in-node heterogeneity of HPC and cloud computing platforms makes software portability and performance optimization extremely challenging. Described here, the MatRIS multilevel math library abstraction framework employs tasking to alleviate these difficulties. MatRIS includes the IRIS task-based runtime on the bottom level and exposes different layers of abstraction to render algorithms architecturally agnostic. MatRIS ensures the decomposition and creation of tasks that represent the necessary encapsulation of the optimized kernels from both vendor and open-source math libraries. Once built, MatRIS can select different combinations of accelerators at runtime, making it portable even on diverse heterogeneous architectures. By leveraging the IRIS runtime’s features for managing heterogeneity, MatRIS deploys algorithms that remove the need to specify orchestration and data transfer. This study describes how the serial task abstraction of a tiled Cholesky factorization is made portable and scalable in the case of multi-device and multi-vendor heterogeneity on a node with NVIDIA and AMD GPUs by using MatRIS. First, we demonstrate that Cholesky in MatRIS provides multi-GPU scalability that offers competitive performance versus cuSolverMG. Then, we present the challenges and opportunities for heterogeneous execution.

Monil, M. A. H.↗

DOE OSTI2013

Application of High Performance Computing for Simulating the Unstable Dynamics of Dilute Spark-Ignited Combustion

In collaboration with a major automotive manufacturer, we are using computational simulations of in-cylinder combustion to understand the multi-scale nonlinear physics of the dilute stability limit. Because some key features of dilute combustion can take thousands of successive cycles to develop, the computation time involved in using complex models to simulate these effects has limited industrys ability to exploit simulations in optimizing advanced engines. We describe a novel approach for utilizing parallel computations to reveal long-timescale features of dilute combustion without the need to simulate many successive engine cycles in series. Our approach relies on carefully guided, concurrent, single-cycle simulations to create metamodels that preserve the long-timescale features of interest. We use a simplified combustion model to develop and demonstrate our strategy for adaptively guiding the concurrent simulations to generate metamodels. We next will implement this strategy with higher-fidelity, multi-scale combustion models on large computing facilities to generate more refined metamodels. The refined metamodels can then be used to accelerate engine development because of their efficiency. Similar approaches might also be used for rapidly exploring the dynamics of other complex multi-scale systems that evolve with serial dependency on time.

Finney, Charles↗

DOE OSTI2014

Fast Multiresolution Reads of Massive Simulation Datasets

Today’s massively parallel simulation codes can produce output ranging up to many terabytes of data. Utilizing this data to support scientific inquiry requires analysis and visualization, yet the sheer size of the data makes it cumbersome or impossible to read without computational resources similar to the original simulation. We identify two broad classes of problems for reading data and present effective solutions for both. The first class of data reads depends on user requirements and available resources. Tasks such as visualization and user-guided analysis may be accomplished using only a subset of variables with a restricted spatial extent at a reduced resolution. The other class of reads requires full resolution multivariate data to be loaded, for example to restart a simulation. We show that utilizing the hierarchical multiresolution IDX data format enables scalable and efficient serial and parallel read access on a variety of hardware from supercomputers down to portable devices. Here, we demonstrate interactive view-dependent visualization and analysis of massive scientific datasets using low-power commodity hardware, and we compare read performance with other parallel file formats for both full and partial resolution data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Computational Nuclear Physics and Post Hartree-Fock Methods

We present a computational approach to infinite nuclear matter employing Hartree-Fock theory, many-body perturbation theory and coupled cluster theory. These lectures are closely linked with those of chapters 9, 10 and 11 and serve as input for the correlation functions employed in Monte Carlo calculations in chapter 9, the in-medium similarity renormalization group theory of dense fermionic systems of chapter 10 and the Green's function approach in chapter 11. We provide extensive code examples and benchmark calculations, allowing thereby an eventual reader to start writing her/his own codes. We start with an object-oriented serial code and end with discussions on strategies for porting the code to present and planned high-performance computing facilities.

Lietz, Justin↗

DOE OSTI2017

Parallelizing Single Source Shortest Path with OpenSHMEM

Single Source Shortest Path (SSSP) is one of the widely occurring graph problems where the paths are discovered from an origin vertex to all other vertices in the graph. In this paper, we discuss our experience parallelizing SSSP using OpenSHMEM. We start with the serial Dijkstra and Bellman-Ford algorithms, parallelize these algorithms, and adapt them to the Partitioned Global Address Space (PGAS) programming model. We implement the parallel algorithms using OpenSHMEM and introduce a series of optimizations to achieve higher scaling and performance characteristics. The implementation is evaluated on Titan with various graphs including synthetic Recursive Matrix (R-MAT) and small-world network graphs as well as real-world graphs from Facebook, Twitter, LiveJournal, and the road maps of California and Texas.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Delaunay walk for fast nearest neighbor: accelerating correspondence matching for ICP

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation.

3d point cloud processing↗

DOE OSTI2015

Three-Dimensional Imaging of Lipids and Metabolites in Tissues by Nanospray Desorption Electrospray Ionization Mass Spectrometry

Abstract Three-dimensional (3D) imaging of tissue sections is a new frontier in mass spectrometry imaging (MSI). Here we report on fast 3D imaging of lipids and metabolites associated with mouse uterine decidual cells and embryo at the implantation site on day 6 of pregnancy. 2D imaging of 16-20 serial tissue sections deposited on the same glass slide was performed using nanospray desorption electrospray ionization (nano-DESI) – an ambient ionization technique that enables sensitive localized analysis of analytes on surfaces without special sample pre-treatment. In this proof-of-principle study, nano-DESI was coupled to a high-resolution Q-Exactive instrument operated at high repetition rate of >5 Hz with moderate mass resolution of 35,000 (m/Δm at m/z 200), which enabled acquisition of the entire 3D image with a spatial resolution of ~150 μm in less than 4.5 hours. The results demonstrate localization of acetylcholine in the primary decidual zone (PDZ) of the implantation site throughout the depth of the tissue examined, indicating an important role of this signaling molecule in decidualization. Choline and phosphocholine – metabolites associated with cell growth – are enhanced in the PDZ and abundant in other cellular regions of the implantation site. Very different 3D distributions were obtained for fatty acids (FA), oleic acid and linoleic acid (FA 18:1 and FA 18:2), differing only by one double bond. Localization of FA 18:2 in the PDZ indicates its important role in decidualization while FA 18:1 is distributed more evenly throughout the tissue. In contrast, several lysophosphatidylcholines (LPC) observed in this study show donut-like distributions with localization around the PDZ. Complementary distributions with minimal overlap were observed for LPC 18:0 and FA 18:2 while the 3D image of the potential precursor phosphatidylcholine (PC 36:2) showed a significant overlap with both LPC 18:0 and FA 18:2.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2015

Profiling of adrenocorticotropic hormone and arginine vasopressin in human pituitary gland and tumor thin tissue sections using droplet-based liquid-microjunction surface-sampling-HPLC–ESI-MS–MS

Described here are the results from the profiling of the proteins arginine vasopressin (AVP) and adrenocorticotropic hormone (ACTH) from normal human pituitary gland and pituitary adenoma tissue sections using a fully automated droplet-based liquid microjunction surface sampling-HPLC-ESI-MS/MS system for spatially resolved sampling, HPLC separation, and mass spectral detection. Excellent correlation was found between the protein distribution data obtained with this droplet-based liquid microjunction surface sampling-HPLC-ESI-MS/MS system and those data obtained with matrix assisted laser desorption ionization (MALDI) chemical imaging analyses of serial sections of the same tissue. The protein distributions correlated with the visible anatomic pattern of the pituitary gland. AVP was most abundant in the posterior pituitary gland region (neurohypophysis) and ATCH was dominant in the anterior pituitary gland region (adenohypophysis). The relative amounts of AVP and ACTH sampled from a series of ACTH secreting and non-secreting pituitary adenomas correlated with histopathological evaluation. ACTH was readily detected at significantly higher levels in regions of ACTH secreting adenomas and in normal anterior adenohypophysis compared to non-secreting adenoma and neurohypophysis. AVP was mostly detected in normal neurohypophysis as anticipated. This work demonstrates that a fully automated droplet-based liquid microjunction surface sampling system coupled to HPLC-ESI-MS/MS can be readily used for spatially resolved sampling, separation, detection, and semi-quantitation of physiologically-relevant peptide and protein hormones, such as AVP and ACTH, directly from human tissue. In addition, the relative simplicity, rapidity and specificity of the current methodology support the potential of this basic technology with further advancement for assisting surgical decision-making.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Transnasal stent-assisted targeting technique for percutaneous jejunostomy placement in patients with hiatal hernias

Purpose: To report the transnasal stent-assisted targeting technique for percutaneous jejunostomy placement in patients with hiatal hernias. Materials and methods: Four patients, including three (75%) females and one (25%) male, with mean age of 77.5 years (range 73–78 years), and with a hiatal hernia and intrathoracic stomach precluding gastrostomy placement and loop snare placement into the mid-jejunum underwent the transnasal stent-assisted targeting technique for percutaneous jejunostomy placement. In all patients, a duodenal stent was inserted into the jejunum in a transnasal fashion. The stent was partially unsheathed in an anterior loop of jejunum and percutaneously targeted using an 18-gauge needle through which a guidewire was advanced, trapped within the stent, and removed through the nose. The tract was serially dilated and a jejunostomy was placed. Technical success, procedure time, fluoroscopy time, radiation exposure, complications, time to enteral feeding, and follow-up were recorded. Results: Technical success was 100% (4/4) with all four patients requiring only one needle pass before successful jejunal cannulation. Mean procedure time was 108 min. Mean fluoroscopy time was 44 min. Mean dose area product was 3969.3 μGym{sup 2}. No minor or major complications occurred. All four patients received enteral feeding one day after the procedure. Mean follow-up was 366 days. Conclusion: The transnasal stent-assisted targeting technique is a novel method for primary jejunostomy placement in patients with hiatal hernias.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Identification of the feeding arteries of hepatocellular carcinomas by performing dual arterial phase CT during pre-transarterial chemoembolization angiography

Purpose: This study evaluated the usefulness of performing dual arterial phase computed tomography (CT) during pre-transarterial chemoembolization (TACE) angiography for identifying the feeding arteries of hepatocellular carcinomas (HCC). Materials and methods: Dual arterial phase CT was performed during pre-TACE angiography in 73 patients with 139 HCC. Ten HCC underwent this procedure twice, so the total number of examined HCC was 149. Early and late arterial phase images were obtained 6 seconds after injection of an iodinated contrast material serially during a single breath-hold. The feeding artery was defined as the branch of the hepatic artery that was connected to the enhanced areas of the tumor. For HCC that could not be visualized on the early arterial phase images, fusion images superimposing the early and late arterial phase images were constructed. Furthermore, technical success defined as successful catheterization of the subsegmental or more distal feeding artery was evaluated. Results: The feeding artery was correctly identified on dual arterial phase CT in 146 of the 149 tumors (98.0%). In two HCC, the feeding arteries could not be identified due to severe motion artifacts, and in one, due to the presence of anastomosis between the right and left hepatic arteries. Catheterization of the subsegmental feeding artery was successful in all TACE procedures (technical success rate: 100%). Conclusion: Performing dual arterial phase CT during angiography appears to be useful for identifying the feeding arteries of HCC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2013

ViennaX: a parallel plugin execution framework for scientific computing

We present the free open source plugin execution framework ViennaX for modularizing and parallelizing scientific simulations. In general, functionality is abstracted by the notion of a task, which is implemented as a plugin. The plugin system facilitates the utilization of both, already available functionality as well as new implementations. Each task can define arbitrary data dependencies which are used by ViennaX to build a task graph. The framework supports the execution of this dependence graph based on the message passing interface in either a serial or a parallel fashion. The applied modular approach allows for defining highly flexible simulations, as plugins can be easily exchanged. The framework's general design as well as implementation details are discussed. Applications based on the Mandelbrot set and the solution of a partial differential equation are investigated, and performance results are shown.

Weinbub, Josef↗

DOE OSTI2017

Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest

Eddy covariance (EC) datasets have provided insight into climate determinants of net ecosystem productivity (NEP) and evapotranspiration (ET) in natural ecosystems for decades, but most EC studies were published in serial fashion such that one study’s result became the following study’s hypothesis. Our approach reflects the hypothetico-deductive process by focusing on previously derived hypotheses. A synthesis of this type of sequential inference reiterates subjective biases and may amplify past assumptions about the role, and relative importance, of cont rols over ecosystem metabolism. Long-term EC datasets facilitate an alternative approach to synthesis: the use of inductive data-based analyses to re-examine past deductive studies of the same ecosystem. Here we examined the seasonal climate determinants of NEP and ET by analyzing a 15-year EC time-series from a subalpine forest using an ensemble of Artificial Neural Networks (ANNs) at the half-day (daytime/nighttime) time-step. We also extracted relative rankings of climate drivers and driver–response relationships directly from the dataset with minimal a priori assumptions. The ANN analysis revealed temperature variables as primary climate drivers of NEP and daytime ET, when all seasons are considered, consistent with the assembly of past studies. New relations uncovered by the ANN approach include the role of soil moisture in driving daytime NEP during the snowmelt period, the nonlinear response of NEP to temperature across seasons, and the low relevance of summer rainfall for NEP or ET at the same daytime/nighttime time step. These new results offer a more complete perspective of climate–ecosystem interactions at this site than traditional deductive analyses alone.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Analysis and modeling of Nannochloropsis growth in lab, greenhouse, and raceway experiments

Efficient production of algal biofuels could reduce dependence on foreign oil by providing a domestic renewable energy source. Moreover, algae-based biofuels are attractive for their large oil yield potential despite decreased land use and natural resource (e.g., water and nutrients) requirements compared to terrestrial energy crops. Important factors controlling algal lipid productivity include temperature, nutrient availability, salinity, pH, and the light-to-biomass conversion rate. Here, computational approaches allow for inexpensive predictions of algae growth kinetics for various bioreactor sizes and geometries without the need for multiple, expensive measurement systems. Parametric studies of algal species include serial experiments that use off-line monitoring of growth and lipid levels. Such approaches are time consuming and usually incomplete, and studies on the effect of the interaction between various parameters on algal growth are currently lacking. However, these are the necessary precursors for computational models, which currently lack the data necessary to accurately simulate and predict algae growth. In this work, we conduct a lab-scale parametric study of the marine alga Nannochloropsis salina and apply the findings to our physics-based computational algae growth model. We then compare results from the model with experiments conducted in a greenhouse tank and an outdoor, open-channel raceway pond. Results show that the computational model effectively predicts algae growth in systems across varying scale and identifies the causes for reductions in algal productivities. Applying the model facilitates optimization of pond designs and improvements in strain selection.

09 BIOMASS FUELS↗

DOE OSTI2017

Effect of Varying Filler Concentration on Zinc Oxide Nanoparticle Embedded Chitosan Films as Potential Food Packaging Material

Prevailing scenario of non-biodegradable food packaging materials worldwide was the motivation for this research. More than half of the packaging materials used today are non-biodegradable and lack one or the other feature that keeps it from being an ideal food packaging material. Based on the current need of food grade packaging materials, the present study illustrates the amelioration of the properties of biodegradable chitosan films with the incorporation of zinc oxide (ZnO) nanoparticles in varying concentration. The ZnO nanoparticles (ZnONPs) used as fillers in the chitosan films were synthesized by supersaturation method. They were characterized using UV–visible spectrophotometry, X-ray diffraction and field emission scanning electron microscopy (FE-SEM). The particles were observed to be around 100–200 nm in size. The chitosan films with varying concentration of ZnONPs were synthesized and characterized using Fourier transform infrared spectroscopy and FE-SEM. The films were studied for their thermal stability, water vapor transmission rate (WVTR) and mechanical properties. The thermal stability, as determined by Thermo Gravimetric Analysis and Differential Scanning Calorimetry increased slightly with increasing percentage of embedded ZnONPs while a substantial decrease in WVTR was observed. Mechanical properties also showed improvements with 77% increment in tensile modulus and 67% increment in tensile strength. The antimicrobial activity of the films was also studied on gram positive bacterium Bacillus subtilis (B. subtilis) and gram negative bacterium Escherichia coli (E. coli) by serial dilution method. A twofold and 1.5-fold increment in the antimicrobial activity was observed for B. subtilis and E. coli, respectively, with increased ZnONPs concentration in the films from 0(w/w) to 2%(w/w). Films thus prepared can prove to be of immense potential in the near future for antimicrobial food packaging applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Predicting the oxidation states of Mn ions in the oxygen-evolving complex of photosystem II using supervised and unsupervised machine learning

Abstract Serial Femtosecond Crystallography at the X-ray Free Electron Laser (XFEL) sources enabled the imaging of the catalytic intermediates of the oxygen evolution reaction of Photosystem II (PSII). However, due to the incoherent transition of the S-states, the resolved structures are a convolution from different catalytic states. Here, we train Decision Tree Classifier and K-means clustering models on Mn compounds obtained from the Cambridge Crystallographic Database to predict the S-state of the X-ray, XFEL, and CryoEM structures by predicting the Mn’s oxidation states in the oxygen-evolving complex. The model agrees mostly with the XFEL structures in the dark S 1 state. However, significant discrepancies are observed for the excited XFEL states (S 2 , S 3, and S 0 ) and the dark states of the X-ray and CryoEM structures. Furthermore, there is a mismatch between the predicted S-states within the two monomers of the same dimer, mainly in the excited states. We validated our model against other metalloenzymes, the valence bond model and the Mn spin densities calculated using density functional theory for two of the mismatched predictions of PSII. The model suggests designing a more optimized sample delivery and illumiation systems are crucial to precisely resolve the geometry of the advanced S-states to overcome the noncoherent S-state transition. In addition, significant radiation damage is observed in X-ray and CryoEM structures, particularly at the dangler Mn center (Mn4). Our model represents a valuable tool for investigating the electronic structure of the catalytic metal cluster of PSII to understand the water splitting mechanism.

Plant Sciences↗