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Toward a Data Scalable Solution for Facilitating Discovery of Science Resources
Science is increasingly motivated by the need to process larger quantities of data. It is facing severe challenges in data collection, management, and processing, so much so that the computational demands of “data scaling” are competing with, and in many fields surpassing, the traditional objective of decreasing processing time. Example domains with large datasets include astronomy, biology, genomics, climate/weather, and material sciences. This paper presents a real-world use case in which we wish to answer queries pro- vided by domain scientists in order to facilitate discovery of relevant science resources. The problem is that the metadata for these science resources is very large and is growing quickly, rapidly increasing the need for a data scaling solution. We propose a system – SGEM – designed for answering graph-based queries over large datasets on cluster architectures, and we re- port performance results for queries on the current RDESC dataset of nearly 1.4 billion triples, and on the well-known BSBM SPARQL query benchmark.
Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO 2 Capture Using Pilot-Plant Data
This paper is focused on the development and validation of a dynamic model for an MEA-based CO 2 capture unit. Starting with a rigorous steady-state process model, the dynamic model is developed in Aspen Plus Dynamics ® using a modified Murphree-efficiency approach and is validated using dynamic data collected from the National Carbon Capture Center (NCCC) in Wilsonville, Alabama. The dynamic test runs were designed with due consideration of process excitation and process nonlinearities constrained by the available time to conduct the test runs and implement the experimental designs in the plant control system. The experimental data include solvent composition and loading. Since the experimental data were found to violate mass and energy balances, filtering algorithms coupled with dynamic data reconciliation techniques were used. Without adjusting any model parameters, the dynamic model satisfactorily predicted dynamic response of CO 2 capture due to step changes in the solvent flowrate, flue gas flowrate, and steam flowrates. Transient studies show that the process gain and time constants can considerably change depending on the direction of the disturbance or manipulated variables, indicating process nonlinearities. Due to a storage tank between the absorber and stripper, the time constant of the full plant can be considerably longer than the time constants of the absorber or stripper individually. Furthermore, impact of the control system on the process efficiency while following an optimal CO 2 capture schedule was also studied.
Do Practical Standard Coupled Cluster Calculations Agree Better than Kohn–Sham Calculations with Currently Available Functionals When Compared to the Best Available Experimental Data for Dissociation Energies of Bonds to 3d Transition Metals?
Coupled-cluster (CC) methods have been extensively used as the high-level approach in quantum electronic structure theory to predict various properties of molecules when experimental results are unavailable. It is often assumed that CC methods, if they include at least up to connected-triple-excitation quasiperturbative corrections to a full treatment of single and double excitations (in particular, CCSD(T)), and a very large basis set, are more accurate than Kohn–Sham (KS) density functional theory (DFT). In the present work, we tested and compared the performance of standard CC and KS methods on bond energy calculations of 20 3d transition metal-containing diatomic molecules against the most reliable experimental data available, as collected in a database called 3dMLBE20. It is found that, although the CCSD(T) and higher levels CC methods have mean unsigned deviations from experiment that are smaller than most exchange-correlation functionals for metal–ligand bond energies of transition metals, the improvement is less than one standard deviation of the mean unsigned deviation. Furthermore, on average, almost half of the 42 exchange-correlation functionals that we tested are closer to experiment than CCSD(T) with the same extended basis set for the same molecule. The results show that, when both relativistic and core–valence correlation effects are considered, even the very high-level (expensive) CC method with single, double, triple, and perturbative quadruple cluster operators, namely, CCSDT(2)Q, averaged over 20 bond energies, gives a mean unsigned deviation (MUD(20) = 4.7 kcal/mol when one correlates only valence, 3p, and 3s electrons of transition metals and only valence electrons of ligands, or 4.6 kcal/mol when one correlates all core electrons except for 1s shells of transition metals, S, and Cl); and that is similar to some good xc functionals (e.g., B97-1 (MUD(20) = 4.5 kcal/mol) and PW6B95 (MUD(20) = 4.9 kcal/mol)) when the same basis set is used. We found that, for both coupled cluster calculations and KS calculations, the T1 diagnostics correlate the errors better than either the M diagnostics or the B1 DFT-based diagnostics. The potential use of practical standard CC methods as a benchmark theory is further confounded by the finding that CC and DFT methods usually have different signs of the error. We conclude that the available experimental data do not provide a justification for using conventional single-reference CC theory calculations to validate or test xc functionals for systems involving 3d transition metals.
What is the best method to fit time-resolved data? A comparison of the residual minimization and the maximum likelihood techniques as applied to experimental time-correlated, single-photon counting data
The need for measuring fluorescence lifetimes of species in subdiffraction-limited volumes in, for example, stimulated emission depletion (STED) microscopy, entails the dual challenge of probing a small number of fluorophores and fitting the concomitant sparse data set to the appropriate excited-state decay function. This need has stimulated a further investigation into the relative merits of two fitting techniques commonly referred to as “residual minimization” (RM) and “maximum likelihood” (ML). Fluorescence decays of the well-characterized standard, rose bengal in methanol at room temperature (530 ± 10 ps), were acquired in a set of five experiments in which the total number of “photon counts” was approximately 20, 200, 1000, 3000, and 6000 and there were about 2–200 counts at the maxima of the respective decays. Each set of experiments was repeated 50 times to generate the appropriate statistics. Each of the 250 data sets was analyzed by ML and two different RM methods (differing in the weighting of residuals) using in-house routines and compared with a frequently used commercial RM routine. Convolution with a real instrument response function was always included in the fitting. While RM using Pearson’s weighting of residuals can recover the correct mean result with a total number of counts of 1000 or more, ML distinguishes itself by yielding, in all cases, the same mean lifetime within 2% of the accepted value. For 200 total counts and greater, ML always provides a standard deviation of <10% of the mean lifetime, and even at 20 total counts there is only 20% error in the mean lifetime. Here, the robustness of ML advocates its use for sparse data sets such as those acquired in some subdiffraction-limited microscopies, such as STED, and, more importantly, provides greater motivation for exploiting the time-resolved capacities of this technique to acquire and analyze fluorescence lifetime data.
Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data
The ever-increasing power of modern super-computers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials.
New Techniques and Data for Understanding the Global Soil Respiration Flux
Abstract Soil respiration ( R s ; the soil surface‐to‐atmosphere CO 2 flux) has been measured in the field for decades, but only recently have we begun to assemble and leverage these small‐scale but extensive data. Recently, Zhao et al. (2017, https://doi.org/10.1002/2016ef000480 ) applied a novel artificial neural network model to the problem of estimating the global R s flux and understanding its variations between regions and biomes. Their results point to a convergence in estimates of global R s , and the power of leveraging the long record of observed R s in global ecosystems, but also to uncertainties about soils' response to climate change. It will take a combination of long‐term studies, data syntheses, modeling intercomparisons, and probably a new generation of sampling networks and experiments to fully resolve these questions.
Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in situ and remote sensing data sets
The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).
Enabling pan-repository reanalysis for big data science of public metabolomics data
Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.
Error reduction of phasor measurement unit data considering practical constraints
Not provided.
Analysis of shockless dynamic compression data on solids to multi-megabar pressures: Application to tantalum
Magnetically-driven, planar shockless-compression experiments to multi-megabar pressures were performed on tantalum samples using a stripline target geometry. Free-surface velocity waveforms were measured in 15 cases; nine of these in a dual-sample configuration with two samples of different thicknesses on opposing electrodes, and six in a single-sample configuration with a bare electrode opposite the sample. Details are given on the application of inverse Lagrangian analysis (ILA) to these data, including potential sources of error. The most significant source of systematic error, particularly for single-sample experiments, was found to arise from the pulse-shape dependent free-surface reflected wave interactions with the deviatoric-stress response of tantalum. This could cause local, possibly temporary, unloading of material from a ramp compressed state, and thus multi-value response in wave speed that invalidates the free-surface to in-material velocity mapping step of ILA. By averaging all 15 data sets, a final result for the principal quasi-isentrope of tantalum in stress-strain was obtained to a peak longitudinal stress of 330 GPa with conservative uncertainty bounds of ±4.5% in stress. The result agrees well with a tabular equation of state developed at Los Alamos National Laboratory.
The NOνA experiment, the first 12 months of commissioning, operations and physics data
The NOνA experiment is a long baseline neutrino experiment designed to make precision measurements of the oscillation probabilities for ν{sub µ} → ν{sub e} and ν{sub µ} → ν{sub µ} for both neutrinos and anti-neutrinos. These measurements will provide new information on the neutrino mass hierarchy, improve our knowledge of whether θ{sub 23} is non-maximal and possibility provide information on the CP violating phase δ{sub CP} of the PMNS neutrino mixing matrix. We present the observations of the first neutrino event in the NOνA far and near detectors along with data obtained during the first year of detector commissioning and operations. We use the data to demonstrate the detector’s ability to identify electron and muon neutrino events and to reject cosmic ray induced backgrounds at a level of 4 × 10{sup 7}:1. New estimates for the signal and background sensitivities of the NOνA experiment during the first year of full detector running are presented.
A novel approach for estimating densities of secretive species from road-survey and spatial-movement data
Accurate estimates of population density are a critical component of effective wildlife conservation and management. However, many snake species are so secretive that their density cannot be determined using traditional methods such as capture–mark–recapture. Thus, the status of most terrestrial snake populations remains completely unknown. We developed a novel simulation-based technique for estimating density of secretive snakes that combined behavioural observations of snake road-crossing behaviour (crossing speed), effort-corrected road-survey data, and simulations of spatial movement patterns derived from radio-telemetry, without relying on mark–recapture. Here, we used radio-telemetry data to parameterise individual-based movement models that estimate the frequency with which individual snakes cross roads and used information on survey vehicle speed and snake crossing speed to determine the probability of detecting a snake, given that it crosses the road transect during a survey. Snake encounter frequencies during systematic road surveys were then interpreted in light of detection probabilities and simulation model results to estimate snake densities and to assess various factors likely to affect abundance estimates. We demonstrated the broad applicability of this approach through a case study of the imperiled southern hognose snake (Heterodon simus) in the North Carolina (USA) Sandhills. We estimated that H. simus occurs at average densities of 0.17 ha –1 in the North Carolina Sandhills and explored the sensitivity of this estimate to assumptions and variation in model parameters. Our novel method allowed us to generate the first abundance estimates for H. simus. We found that H. simus exists at low densities relative to congeners and other mid-sized snake species, raising concern that this species may not only have declined in geographic range, but may also occur at low densities or be declining in their strongholds, such as the North Carolina Sandhills. We present a framework for estimating density of species that have traditionally been considered too secretive to study at the population level. This method will greatly enhance our ability to study and manage a wide variety of snake species and could be applied to other secretive wildlife species that are most frequently encountered during road surveys.
THE SLOAN DIGITAL SKY SURVEY STRIPE 82 IMAGING DATA: DEPTH-OPTIMIZED CO-ADDS OVER 300 deg{sup 2} IN FIVE FILTERS
We present and release co-added images of the Sloan Digital Sky Survey (SDSS) Stripe 82. Stripe 82 covers an area of ∼300 deg{sup 2} on the celestial equator, and has been repeatedly scanned 70-90 times in the ugriz bands by the SDSS imaging survey. By making use of all available data in the SDSS archive, our co-added images are optimized for depth. Input single-epoch frames were properly processed and weighted based on seeing, sky transparency, and background noise before co-addition. The resultant products are co-added science images and their associated weight images that record relative weights at individual pixels. The depths of the co-adds, measured as the 5σ detection limits of the aperture (3.''2 diameter) magnitudes for point sources, are roughly 23.9, 25.1, 24.6, 24.1, and 22.8 AB magnitudes in the five bands, respectively. They are 1.9-2.2 mag deeper than the best SDSS single-epoch data. The co-added images have good image quality, with an average point-spread function FWHM of ∼1'' in the r, i, and z bands. We also release object catalogs that were made with SExtractor. These co-added products have many potential uses for studies of galaxies, quasars, and Galactic structure. We further present and release near-IR J-band images that cover ∼90 deg{sup 2} of Stripe 82. These images were obtained using the NEWFIRM camera on the NOAO 4 m Mayall telescope, and have a depth of about 20.0-20.5 Vega magnitudes (also 5σ detection limits for point sources)
Data quality assurance for the Majorana Demonstrator
The Majorana Demonstrator is an experiment constructed to search for neutrinoless double-beta decays in germanium-76 and to demonstrate the feasibility to deploy a large-scale experiment in a phased and modular fashion. It consists of two modular arrays of natural and 7648 Ge-enriched germanium detectors totalling 44.1 kg, located at the 4850' level of the Sanford Underground Research Facility in Lead, South Dakota, USA. Any Neutrinoless double-beta decay search requires a thorough understanding of the background and the signal energy spectra. This talk will discuss the various techniques employed to ensure the integrity of the measured spectra. Data collection is monitored with a thorough set of checks, and subsequent careful analysis is performed to qualify the data for higher level physics analysis. Instrumental background events are tagged for removal, and problematic channels are removed from consideration as necessary.
Data processing workflow for time of flight polarized neutrons inelastic measurements
We discuss the data processing workflow for polarized neutron scattering measurements performed at HYSPEC spectrometer at the Spallation Neutron Source, Oak Ridge National Laboratory. The effects of the focusing Heusler crystal polarizer and the wide-angle supermirror transmission polarization analyzer are added to the data processing flow of the non-polarized case. The implementation is done using the Mantid software package.
Monitoring and data quality assessment of the ATLAS liquid argon calorimeter
The liquid argon calorimeter is a key component of the ATLAS detector installed at the CERN Large Hadron Collider. The primary purpose of this calorimeter is the measurement of electron and photon kinematic properties. It also provides a crucial input for measuring jets and missing transverse momentum. An advanced data monitoring procedure was designed to quickly identify issues that would affect detector performance and ensure that only the best quality data are used for physics analysis. This article presents the validation procedure developed during the 2011 and 2012 LHC data-taking periods, in which more than 98% of the proton-proton luminosity recorded by ATLAS at a centre-of-mass energy of 7-8 TeV had calorimeter data quality suitable for physics analysis.
Circumventing data imbalance in magnetic ground state data for magnetic moment predictions
Abstract Magnetic materials play a crucial role in the transition to more sustainable forms of energy and electric vehicles. There is an anticipated shortage in magnetic materials in the future, and as a result there is an urgent need to discover and design new magnetic materials. Computational magnetic material design using density functional theory is daunting because of the challenge in identifying magnetic ground states from a combinatorially large set of possibilities. Machine learning offers a path forward by enabling efficient surrogate models that can more readily enumerate these states, but there is a dearth of training data available, and what is available tends to be imbalanced with too much non-magnetic data. In this work we show that the discrete and previously tackled data imbalance that exists at the level of the magnetic ordering leads to an imbalanced continuous distribution with many zeros when the data is unraveled at the atomic magnetic moment level, which subsequently leads to models with low accuracy for magnetic properties. We mitigate this by using a two-part model framework. Our scheme is able to classify atoms into magnetic and non-magnetic with an F1 score and Matthew’s correlation coefficient (MCC) of ~91% and then to provide an implicit embedding representation that maps directly onto the magnitude of the magnetic moment with a mean absolute error of 0.1 μ B . Beyond screening for new magnetic materials, we demonstrate an additional practical use case of our scheme: the provision of good initial guesses for magnetic moments in first-principles electronic relaxations. Such initialization is shown to lead to faster convergence to configurations that lie closer to the ground state.