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DOE OSTI2021

Significant DBSCAN+: Statistically Robust Density-based Clustering

Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.

Computer Science↗

DOE OSTI2016

Using the H Index to Assess Impact of DOE National Laboratories

The most readily accessible elements of the Emerald Matrix by quantitative measures are the knowledge and economy related measures. In this paper, the H Index for an institution will be used to assess STE impact, which is in the knowledge generation element. The H Index was developed by Hirsch (2005) as a measure of an individual’s scientific impact. The H Index is defined as the number of publications that have been cited h or more times for a given author. It has been generalized to organizations. Doing so leads to a complication in that H index scales with the number of publications. Although this may not be problematic when comparing individual researchers, it systematically favors larger institutions. Molinari and Molinari (2008) proposed an alternative index (hm) designed to assess organizational impact. It transforms the H Index for an organization into an impact index by removing a factor dependent on the number of publications. The hm provides another approach to compare institutions provided that differences in the citation patterns associated with fields of study are addressed. Kinney (2007) used the Molinari and Molinari (2008) approach to compare various scientific institutions in nonbiomedical research areas. Kinney (2007) used the Thomson Reuters Web of Science (WoS) as the source and used publications in nonbiomedical research areas, which is very important because the research areas of universities are much broader than say a DOE national laboratory. Also there are differences in citation rates for the various research fields that make comparisons between individuals or organizations difficult. The results from Kinney (2007) are given in Table 1 and indicate that the DOE national laboratories compare favorably with the selected universities in terms of impact (hm) in the research areas used in Kinney’s analysis. This report will compare hm for DOE national laboratories using an approach similar to Kinney (2007) providing a measure of impact of the DOE national laboratories.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part I: design principles and rapid down-selection

Abstract Refractory metal-based multi-principal element alloys (MPEAs) are compelling materials for high-temperature (1000–2000 K) structural applications. However, only a minuscule fraction of their vast and heterogeneous compositional design space has been explored, leaving many potentially interesting alloys undiscovered. In this two-part work, a large region of the 11-element Al-Cr-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr design space is computationally explored to identify refractory MPEAs with simultaneously high yield strength or specific yield strength and body-centered cubic (BCC) solid solution stability. In Part I , two case studies illuminate key factors and considerations in the yield strength versus phase stability tradeoff, provide guidelines for narrowing the expansive design space, and identify many candidates predicted to be stronger than refractory MPEAs reported to date, with BCC phase stability. Our findings indicate that medium entropy ternary alloys can outperform alloys with more elements and highlight the importance of exploring regions away from the equiatomic center of composition space.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Measurement of the effective leptonic weak mixing angle

Using pp collision data at $\sqrt{s}$ = 13 TeV, recorded by the LHCb experiment between 2016 and 2018 and corresponding to an integrated luminosity of 5.4 fb −1 , the forward-backward asymmetry in the pp → Z/γ* → μ + μ − process is measured. The measurement is carried out in ten intervals of the difference between the muon pseudorapidities, within a fiducial region covering dimuon masses between 66 and 116 GeV, muon pseudorapidities between 2.0 and 4.5 and muon transverse momenta above 20 GeV. These forward-backward asymmetries are compared with predictions, at next-to-leading order in the strong and electroweak couplings. The measured effective leptonic weak mixing angle is ${\sin}^2{\theta}_{\textrm{eff}}^{\ell }=0.23147\pm 0.00044\pm 0.00005\pm 0.00023,$ where the first uncertainty is statistical, the second arises from systematic uncertainties associated with the asymmetry measurement, and the third arises from uncertainties in the fit model used to extract ${\sin}^2{\theta}_{\textrm{eff}}^{\ell }$ from the asymmetry measurement. This result is based on an arithmetic average of results using the CT18, MSHT20, and NNPDF31 parameterisations of the proton internal structure, and is consistent with previous measurements and with predictions from the global electroweak fit.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Dark Energy Survey Year 6 results: cell-based coadds and METADETECTION weak lensing shape catalogue

We present the metadetection weak lensing galaxy shape catalogue from the 6-yr Dark Energy Survey (DES Y6) imaging data. This data set is the final release from DES, spanning 4422 deg 2 of the southern sky. We describe how the catalogue was constructed, including the two new major processing steps, cell-based image coaddition, and shear measurements with metadetection. The DES Y6 M etadetection weak lensing shape catalogue consists of 151 922 791 galaxies detected over riz bands, with an effective number density of n eff = 8.22 galaxies per arcmin 2 and shape noise of σ e = 0.29. We carry out a suite of validation tests on the catalogue, including testing for point spread function (PSF) leakage, testing for the impact of PSF modelling errors, and testing the correlation of the shear measurements with galaxy, PSF, and survey properties. In addition to demonstrating that our catalogue is robust for weak lensing science, we use the DES Y6 image simulation suite to estimate the overall multiplicative shear bias of our shear measurement pipeline. We find no detectable multiplicative bias at the roughly half-per cent level, with m = (3.4 ± 6.1) x 10 –3 , at 3σ uncertainty. This is the first time both cell-based coaddition and Metadetection algorithms are applied to observational data, paving the way to the Stage-IV weak lensing surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Pressureless sintering of lithium hydride

Lithium Hydride is a material of growing importance for addressing technological challenges related to nuclear fusion, long-term human space travel, and thermal energy storage. Pressureless sintering provides a straightforward, scalable approach to produce dense LiH parts of all sizes and shapes. Pressed LiH green compacts were sintered at heating rates from 2.5 to 20 °C/min to 650 °C, yielding densities up to 96 ± 1.4 %, with densification initiating at 500 °C. Here, a validated master sintering curve was constructed with a sintering apparent activation energy of 135 kJ/mol. X-ray diffraction and simultaneous thermal analysis revealed Li 2 O formation from 300 – 550 °C and decomposition of LiH into Li metal at 550 °C, each reflected as deviations in the master sintering curve. Computed tomography after thermal treatment to 550 °C showed the formation of corrosion products, and after thermal treatment to 650 °C LiH reduction to Li most significantly at exposed surfaces.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Study of second phase in bioabsorbable magnesium alloys: Phase stability evaluation via Dmol{sup 3} calculation

Thermodynamical stabilities of four conventional second phases as well as magnesium matrix in bioabsorbable magnesium alloys were investigated theoretically via computer calculation method. Model of individual phase and systems including phase and four water molecular (phase-4H{sub 2}O) were established to simulate the in vitro and in vivo environment. Local orbital density functional theory approach was applied to calculate the total energy for the individual phase and phase-4H{sub 2}O system. The results demonstrated that all the second phases possessed higher phase stability compared with magnesium matrix, but the phase stability was quite different for different types of second phases or second phase-4H{sub 2}O systems. Furthermore, a schematic process of inflammation reaction caused by magnesium alloy implants was proposed for the further evaluation on biocompatibility of different second phases.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Full-scale computation for all the thermoelectric property parameters of half-Heusler compounds

The thermoelectric performance of materials relies substantially on the band structures that determine the electronic and phononic transports, while the transport behaviors compete and counter-act for the power factor PF and figure-of-merit ZT. These issues make a full-scale computation of the whole set of thermoelectric parameters particularly attractive, while a calculation scheme of the electronic and phononic contributions to thermal conductivity remains yet challenging. In this work, we present a full-scale computation scheme based on the first-principles calculations by choosing a set of doped half- Heusler compounds as examples for illustration. The electronic structure is computed using the WIEN2k code and the carrier relaxation times for electrons and holes are calculated using the Bardeen and Shockley’s deformation potential (DP) theory. The finite-temperature electronic transport is evaluated within the framework of Boltzmann transport theory. In sequence, the density functional perturbation combined with the quasi-harmonic approximation and the Klemens’ equation is implemented for calculating the lattice thermal conductivity of carrier-doped thermoelectric materials such as Tidoped NbFeSb compounds without losing a generality. The calculated results show good agreement with experimental data. Lastly, the present methodology represents an effective and powerful approach to calculate the whole set of thermoelectric properties for thermoelectric materials.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

Few hydrogen adsorbents balance high usable volumetric and gravimetric capacities. Although metal-organic frameworks (MOFs) have recently demonstrated progress in closing this gap, the large number of MOFs has hindered the identification of optimal materials. Here, a systematic assessment of published databases of real and hypothetical MOFs is presented. Nearly 500,000 compounds were screened computationally, and the most promising were assessed experimentally. Three MOFs with capacities surpassing that of IRMOF-20, the record-holder for balanced hydrogen capacity, are demonstrated: SNU-70, UMCM-9, and PCN-610/NU-100. Analysis of trends reveals the existence of a volumetric ceiling at ~ 40 g H 2 L -1 . Surpassing this ceiling is proposed as a new capacity target for hydrogen adsorbents. Counter to earlier studies of total hydrogen uptake in MOFs, usable capacities in the highest-capacity materials are negatively correlated with density and volumetric surface area. Instead, capacity is maximized by increasing gravimetric surface area and porosity. This suggests that property/performance trends for total capacities may not translate to usable capacities.

36 MATERIALS SCIENCE↗

DOE OSTI2018

The 2019 materials by design roadmap

Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. Increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. Perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this roadmap will guide researchers and funding agencies in identifying new prospects for materials design.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Modeling antiphase boundary energies of Ni 3 Al-based alloys using automated density functional theory and machine learning

Antiphase boundaries (APBs) are planar defects that play a critical role in strengthening Ni-based superalloys, and their sensitivity to alloy composition offers a flexible tuning parameter for alloy design. Here, we report a computational workflow to enable the development of sufficient data to train machine-learning (ML) models to automate the study of the effect of composition on the (111) APB energy in Ni 3 Al-based alloys. We employ ML to leverage this wealth of data and identify several physical properties that are used to build predictive models for the APB energy that achieve a cross-validation error of 0.033 J m –2 . We demonstrate the transferability of these models by predicting APB energies in commercial superalloys. Moreover, our use of physically motivated features such as the ordering energy and stoichiometry-based features opens the way to using existing materials properties databases to guide superalloy design strategies to maximize the APB energy.

36 MATERIALS SCIENCE↗

DOE OSTI2022

The effect of polymer stiffness on magnetization reversal of magnetorheological elastomers

Ultrasoft magnetorheological elastomers (MREs) offer convenient real-time magnetic field control of mechanical properties that provides a means to mimic mechanical cues and regulators of cells in vitro. Here, we systematically investigate the effect of polymer stiffness on magnetization reversal of MREs using a combination of magnetometry measurements and computational modeling. Poly-dimethylsiloxane-based MREs with Young’s moduli that range over two orders of magnitude were synthesized using commercial polymers Sylgard™ 527, Sylgard 184, and carbonyl iron powder. The magnetic hysteresis loops of the softer MREs exhibit a characteristic pinched loop shape with almost zero remanence and loop widening at intermediate fields that monotonically decreases with increasing polymer stiffness. A simple two-dipole model that incorporates magneto-mechanical coupling not only confirms that micrometer-scale particle motion along the applied magnetic field direction plays a defining role in the magnetic hysteresis of ultrasoft MREs but also reproduces the observed loop shapes and widening trends for MREs with varying polymer stiffnesses.

36 MATERIALS SCIENCE↗

DOE OSTI2022

PyAMG: Algebraic Multigrid Solvers in Python

PyAMG is a Python package of algebraic multigrid (AMG) solvers and supporting tools for approximating the solution to large, sparse linear systems of algebraic equations, Ax = b, where A is an n × n sparse matrix. Sparse linear systems arise in a range of problems in science, from fluid flows to solid mechanics to data analysis. While the direct solvers available in SciPy’s sparse linear algebra package (scipy.sparse.linalg) are highly efficient, in many cases iterative methods are preferred due to overall complexity. However, the iterative methods in SciPy, such as CG and GMRES, often require an efficient preconditioner in order to achieve a lower complexity. Preconditioning is a powerful tool whereby the conditioning of the linear system and convergence rate of the iterative method are both dramatically improved. PyAMG constructs multigrid solvers for use as a preconditioner in this setting. A summary of multigrid and algebraic multigrid solvers can be found in Olson (2015a), in Olson (2015b), and in Falgout (2006); a detailed description can be found in Briggs et al. (2000) and Trottenberg et al. (2001).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2013

DBSI: DNA-binding site identifier

In this study, we present the DNA-Binding Site Identifier (DBSI), a new structure-based method for predicting protein interaction sites for DNA binding. DBSI was trained and validated on a data set of 263 proteins (TRAIN-263), tested on an independent set of protein-DNA complexes (TEST-206) and data sets of 29 unbound (APO-29) and 30 bound (HOLO-30) protein structures distinct from the training data. We computed 480 candidate features for identifying protein residues that bind DNA, including new features that capture the electrostatic microenvironment within shells near the protein surface. Our iterative feature selection process identified features important in other models, as well as features unique to the DBSI model, such as a banded electrostatic feature with spatial separation comparable with the canonical width of the DNA minor groove. Validations and comparisons with established methods using a range of performance metrics clearly demonstrate the predictive advantage of DBSI, and its comparable performance on unbound (APO-29) and bound (HOLO-30) conformations demonstrates robustness to binding-induced protein conformational changes. Finally, we offer our feature data table to others for integration into their own models or for testing improved feature selection and model training strategies based on DBSI.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Neutron sub-micrometre tomography from scattering data

Neutrons are valuable probes for various material samples across many areas of research. Neutron imaging typically has a spatial resolution of larger than 20 µm, whereas neutron scattering is sensitive to smaller features but does not provide a real-space image of the sample. A computed-tomography technique is demonstrated that uses neutron-scattering data to generate an image of a periodic sample with a spatial resolution of ∼300 nm. The achieved resolution is over an order of magnitude smaller than the resolution of other forms of neutron tomography. This method consists of measuring neutron diffraction using a double-crystal diffractometer as a function of sample rotation and then using a phase-retrieval algorithm followed by tomographic reconstruction to generate a map of the sample's scattering-length density. Topological features found in the reconstructions are confirmed with scanning electron micrographs. This technique should be applicable to any sample that generates clear neutron-diffraction patterns, including nanofabricated samples, biological membranes and magnetic materials, such as skyrmion lattices.

nanoscience↗

DOE OSTI2021

High-throughput design of high-performance lightweight high-entropy alloys

Developing affordable and light high-temperature materials alternative to Ni-base superalloys has significantly increased the efforts in designing advanced ferritic superalloys. However, currently developed ferritic superalloys still exhibit low high-temperature strengths, which limits their usage. Here we use a CALPHAD-based high-throughput computational method to design light, strong, and low-cost high-entropy alloys for elevated-temperature applications. Through the high-throughput screening, precipitation-strengthened lightweight high-entropy alloys are discovered from thousands of initial compositions, which exhibit enhanced strengths compared to other counterparts at room and elevated temperatures. The experimental and theoretical understanding of both successful and failed cases in their strengthening mechanisms and order-disorder transitions further improves the accuracy of the thermodynamic database of the discovered alloy system. This study shows that integrating high-throughput screening, multiscale modeling, and experimental validation proves to be efficient and useful in accelerating the discovery of advanced precipitation-strengthened structural materials tuned by the high-entropy alloy concept.

36 MATERIALS SCIENCE↗