Basin-informed flood frequency analysis using deep learning exhibits consistent projected regional patterns over CONUS
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54 ENVIRONMENTAL SCIENCES↗
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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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Cluster analysis of occupancy schedules in residential buildings in the United States
The energy performance of residential buildings significantly depends on the building occupants’ behavior, which can be highly variable. When the heating, ventilation and air conditioning (HVAC) system is controlled based on the presence or absence of occupants in a building, occupant behavior is of even further importance to its energy performance. In current practice, building energy simulation tools generally use a single occupancy profile to represent the building’s occupancy schedule, the schedule of which is considered to be the same, regardless of the type of household being modeled. Thus, there is significant potential for improvement to allow for more flexibility and accuracy in calculation of occupancy. The objective of this study is to assess the variations in the typical types of occupancy schedules followed by the U.S. population using cluster analysis. American Time Use Survey data, which statically represents the overall U.S. population’s activities, across 12 years (2006–2017), is used. The ATUS data is segregated into smaller groups based on age and weekday/weekend, then divided into activities that are considered “at home” and “away from home”, which are mapped to the presence or non-presence of occupants in the home. Cluster analysis is then used to identify common types of occupancy schedule patterns for each age group. Three main types of patterns are obtained from cluster analysis for each age group, which together represent approximately 88% of people in the United States. The output of the cluster analysis is further analyzed to evaluate the variation in characteristics, including the number of times leaving home, time of day when leaving the home, and the timespan of absence from the home. The results of this study provide detailed insights on how typical occupants in the United States spend their time in residential spaces which can be used to create occupancy profiles for residential buildings. Finally, these occupancy profiles could be utilized inform an assessment of the energy use impact of occupancy-based controls of energy consuming systems and technologies.
42 ENGINEERING↗
Synthesis of magnesiowüstite nanocrystallites embedded in an amorphous silicate matrix via low energy multiple ion implantations
The synthesis process is presented for experimentally simulating modifications in cosmic dust grains using sequential ion implantations or irradiations followed by thermal annealing. Cosmic silicate dust analogues were prepared via implantation of 20–80 keV Fe - , Mg - , and O - ions into commercially available p-type silicon (100)wafers. The as-implanted analogues are amorphous with a Mg/(Fe+Mg) ratio of 0.5 tailored to match theoretical abundances in circumstellar dusts. Before the ion implantations were performed, Monte-Carlo-based ion-solid interaction codes were used to model the dynamic redistribution of the implanted atoms in the silicon substrate. 600 keV helium ion irradiation was performed on one of the samples before thermal annealing. Two samples were thermally annealed at a temperature appropriate for an M-class stellar wind, 1000 K, for 8.3 h in a vacuum chamber with a pressure of 1 x 10 -7 torr. The elemental depth profiles were extracted utilizing Rutherford Backscattering Spectrometry (RBS) in the samples before and after thermal annealing. X-ray diffraction (XRD)analysis was employed for the identification of various phases in crystalline minerals in the annealed analogues. Transmission electron microscopy (TEM) analysis was utilized to identify specific crystal structures. RBS analysis shows redistribution of the implanted Fe, Mg, and O after thermal annealing due to incorporation into the crystal structures for each sample type. XRD patterns along with TEM analysis showed nanocrystalline Mg and Fe oxides with possible incorporation of additional silicate minerals.
79 ASTRONOMY AND ASTROPHYSICS↗
Exploring the High-Pressure Phases of Carbon through X-ray Diffraction of Dynamic Compression Experiments on Sandia’s Z Pulsed Power Facility
The carbon phase diagram is rich with polymorphs which possess very different physical and optical properties ideal for different scientific and engineering applications. An understanding of the dynamically driven phase transitions in carbon is particularly important for applications in inertial confinement fusion, as well as planetary and meteorite impact histories. Experiments on the Z Pulsed Power Facility at Sandia National Laboratories generate dynamically compressed high-pressure states of matter with exceptional uniformity, duration, and size that are ideal for investigations of fundamental material properties. X-ray diffraction (XRD) is an important material physics measurement because it enables direct observation of the strain and compression of the crystal lattice, and it enables the detection and identification of phase transitions. Several unique challenges of dynamic compression experiments on Z prevent using XRD systems typically utilized at other dynamic compression facilities, so novel XRD diagnostics have been designed and implemented. We performed experiments on Z to shock compress carbon (pyrolytic graphite) samples to pressures of 150–320 GPa. The Z-Beamlet Laser generated Mn-Heα (6.2 keV) X-rays to probe the shock-compressed carbon sample, and the new XRD diagnostics measured changes in the diffraction pattern as the carbon transformed into its high-pressure phases. Quantitative analysis of the dynamic XRD patterns in combination with continuum velocimetry information constrained the stability fields and melting of high-pressure carbon polymorphs.
36 MATERIALS SCIENCE↗
Distributed non-negative matrix factorization with determination of the number of latent features
The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.
97 MATHEMATICS AND COMPUTING↗
Frustrated magnetic cycloidal structure and emergent Potts nematicity in CaMn 2 P 2
We report neutron-diffraction results on single-crystal CaMn 2 P 2 containing corrugated Mn honeycomb layers, and we determine its ground-state magnetic structure. The diffraction patterns consist of prominent (1/6,1/6, L ) reciprocal-lattice unit (r.l.u.; L = integer) magnetic Bragg reflections, whose temperature-dependent intensities are consistent with a first-order antiferromagnetic phase transition at the Néel temperature T N = 70 (1) K. Our analysis of the diffraction patterns reveals an in-plane 6 × 6 magnetic unit cell with ordered spins that in the principal-axis directions rotate by 60°steps between nearest neighbors on each sublattice that forms the honeycomb structure, consistent with the P A c magnetic space group. We find that a few other magnetic subgroup symmetries (P A 2 /c, P C 2/m, P S 1, P C 2, P C m, P S 1) of the paramagnetic $P\bar{3}m11'$ crystal symmetry are consistent with the observed diffraction pattern. We relate our findings to frustrated J 1 -J 2 -J 3 Heisenberg honeycomb antiferromagnets with single-ion anisotropy and the emergence of Potts nematicity.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Graph characterization of higher-order structure in atmospheric chemical reaction mechanisms
Atmospheric chemical reactions play an important role in air quality and climate change. While the structure and dynamics of individual chemical reactions are fairly well understood, the emergent properties of the entire atmospheric chemical system, which can involve many different species that participate in many different reactions, are not well described. In this work, we leverage graph-theoretic techniques to characterize patterns of interaction (“motifs”) in three different representations of gas-phase atmospheric chemistry, termed “chemical mechanisms.” These widely used mechanisms, the master chemical mechanism, the GEOS-Chem mechanism, and the Super-Fast mechanism, vary dramatically in scale and application, but they all generally aim to simulate the abundance and variability of chemical species in the atmosphere. This motif analysis quantifies the fundamental patterns of interaction within the mechanisms, which are directly related to their construction. For example, the gas-phase chemistry in the very small Super-Fast mechanism is entirely composed of bimolecular reactions, and its motif distribution matches that of an individual bimolecular reaction well. The larger and more complex mechanisms show emergent motif distributions that differ strongly from any specific reaction type, consistent with their complexity. The proposed motif analysis demonstrates that while these mechanisms all have a similar design goal, their higher-order structure of interactions differs strongly and thus provides a novel set of tools for exploring differences across chemical mechanisms.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Peri-Net-Pro: the neural processes with quantified uncertainty for crack patterns
Abstract This paper develops a deep learning tool based on neural processes (NPs) called the Peri-Net-Pro, to predict the crack patterns in a moving disk and classifies them according to the classification modes with quantified uncertainties. In particular, image classification and regression studies are conducted by means of convolutional neural networks (CNNs) and NPs. First, the amount and quality of the data are enhanced by using peridynamics to theoretically compensate for the problems of the finite element method (FEM) in generating crack pattern images. Second, case studies are conducted with the prototype microelastic brittle (PMB), linear peridynamic solid (LPS), and viscoelastic solid (VES) models obtained by using the peridynamic theory. The case studies are performed to classify the images by using CNNs and determine the suitability of the PMB, LBS, and VES models. Finally, a regression analysis is performed on the crack pattern images with NPs to predict the crack patterns. The regression analysis results confirm that the variance decreases when the number of epochs increases by using the NPs. The training results gradually improve, and the variance ranges decrease to less than 0.035. The main finding of this study is that the NPs enable accurate predictions, even with missing or insufficient training data. The results demonstrate that if the context points are set to the 10th, 100th, 300th, and 784th, the training information is deliberately omitted for the context points of the 10th, 100th, and 300th, and the predictions are different when the context points are significantly lower. However, the comparison of the results of the 100th and 784th context points shows that the predicted results are similar because of the Gaussian processes in the NPs. Therefore, if the NPs are employed for training, the missing information of the training data can be supplemented to predict the results.
Mathematics↗
Unsupervised anomaly detection in MeV ultrafast electron diffraction
MeV ultrafast electron diffraction (MUED) is a pump-probe technique used to study the dynamic structural evolution of materials. An ultrashort laser pulse triggers structural changes, which are then probed by an ultrashort relativistic electron beam. To overcome low signal-to-noise ratios, diffraction patterns are averaged over thousands of shots. However, shot-to-shot instabilities in the electron beam can distort individual patterns, introducing uncertainty. Improving MUED accuracy requires detecting and removing these anomalous patterns from large datasets. In this work, we developed a fully unsupervised methodology for the detection of anomalous diffraction patterns. Using a convolutional autoencoder, we calculate the reconstruction mean squared error of the diffraction patterns. Based on the statistical analysis of this error, we provide the user an estimation of the probability that the pattern is normal, which also allows a posterior visual inspection of the images that are difficult to classify. This method has been trained with only 100 diffraction patterns and tested on 1521 patterns, resulting in a false positive rate between 0.2% and 0.4%, with a training time of 10 s per image and a test time of about 1 s per image. Here, the proposed methodology can also be applied to other diffraction techniques in which large datasets are collected that include faulty images due to instrumental instabilities.
43 PARTICLE ACCELERATORS↗
The Complexity and Challenges of Irradiation-Induced Phase Instability Phenomenon in Nuclear Power Plant Components
High energy irradiation by neutrons or ions of metastable austenitic 300-series steels appears to initiate formation of bcc-phases with different morphology and elemental composition compared to that of retained ferrite. Extensive formation of specific Fe-rich ferrite was observed on the grain boundaries of annealed Russian steel 12Cr18Ni10Ti, an analog of AISI 321, after irradiation in the center-core region of sodium-cooled BN-350 fast reactor (SFR) doses up to 57 dpa at ~1×10-6dpa/sec. The ferrite fraction determined from scanning electron microscopy (SEM) closely matched the magnetic fraction determined using a ferritscope confirmed by X-ray diffraction analysis and selected area diffraction patterns. Chemical composition of the secondary phases was determined by energy dispersive X-ray analysis using transmission electron microscope, showing Fe-rich ferrite domains. Fe-ion irradiation (2.3 MeV) was used to study the accumulation of ferrite and martensite phases at higher dpa rates. The very high etchability of these ferrite particles in alcohol-based solutions may signal a very high etching in hot water, leading to accelerated intergranular cracking, especially upon long-term exposure during extended plant lifetimes.
D a, Merezhko↗
Using Temporal Information from Human Mobility Data to Detect Anchor Points
Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.
McBride, Liz [ORNL] (ORCID:0000000286925869)↗
Switching interlayer magnetic order in bilayer CrI 3 by stacking reversal
CrI 3 , a hot two-dimensional (2D) magnet, exhibits complex magnetism depending on the number of layers and interlayer stacking patterns. For bilayer CrI 3 , the interlayer magnetism can be tuned between ferromagnetic (FM) and antiferromagnetic (AFM) order by manipulating the stacking order. However, the stacking is mostly modified through translation between the layers, while the effect of rotation between the layers on the interlayer magnetic order has not yet been fully investigated. Here, we considered three energetically stable stacking patterns R$\bar3$ with combining macron], C2/m and AA in bilayer CrI 3 , and their reversed counterparts R$\bar3$ with combining macron]-r, C2/m-r and AA-r through rotating one layer by 180° with respect to the other layer. Our first-principles calculations suggest that the interlayer magnetic ground state can be switched from AFM to FM (or FM to AFM) by reversing the stacking pattern. A detailed microscopic analysis was carried out by magnetic force theory calculations on C2/m stacking which favors AFM and C2/m-r stacking which favors FM. The interlayer magnetic interactions and the origin of the magnetic order change were revealed through specific orbital analysis. Our work demonstrates that stacking rotation can also tune the interlayer magnetism of CrI 3 and provides insight into its interlayer magnetic properties at the microscopic level.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering
Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.
36 MATERIALS SCIENCE↗
The metabolic origins of non-photorespiratory CO 2 release during photosynthesis: a metabolic flux analysis
Respiration in the light (R L ) releases CO 2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since R L lowers net carbon fixation, understanding R L could help improve plant carbon-use efficiency and models of crop photosynthesis. Although R L was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to R L , we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13 CO 2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO 2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N 2 ; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that R L releases 5.2 μmol CO2 g –1 FW h –1 of CO 2 , which is relatively consistent with a value of 9.3 μmol CO 2 g –1 FW h –1 measured by CO 2 gas exchange. The results indicated that ≤10% of R L results from TCA cycle reactions, which are widely considered to dominate R L . Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >% of CO 2 released by R L .
59 BASIC BIOLOGICAL SCIENCES↗
A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange
This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.
Chavez, Tanny (ORCID:0000000193172896)↗
Complementary workflows for identifying one-hop network behavior and multi-hop network dependencies
A network analysis tool evaluates network flow information in complementary workflows to identify one-hop behavior of network assets and also identify multi-hop dependencies between network assets. In one workflow (e.g., using association rule learning), the network analysis tool can identify significant one-hop communication patterns to and/or from network assets, taken individually. Based on the identified one-hop behavior, the network analysis tool can discover patterns of similar communication among different network assets, which can inform decisions about deploying patch sets, mitigating damage, configuring a system, or detecting anomalous behavior. In a different workflow (e.g., using deep learning or cross-correlation analysis), the network analysis tool can identify significant multi-hop communication patterns that involve network assets in combination. Based on the identified multi-hop dependencies, the network analysis tool can discover functional relationships between network assets, which can inform decisions about configuring a system, managing critical network assets, or protecting critical network assets.
97 MATHEMATICS AND COMPUTING↗
Exploring few and single layer CrPS 4 with near-field infrared spectroscopy
Abstract We combine synchrotron-based near-field infrared spectroscopy and first principles lattice dynamics calculations to explore the vibrational response of CrPS 4 in bulk, few-, and single-layer form. Analysis of the mode pattern reveals a C 2 polar + chiral space group, no symmetry crossover as a function of layer number, and a series of non-monotonic frequency shifts in which modes with significant intralayer character harden on approach to the ultra-thin limit whereas those containing interlayer motion or more complicated displacement patterns soften and show inflection points or steps. This is different from MnPS 3 where phonons shift as 1/size 2 and are sensitive to the three-fold rotation about the metal center that drives the symmetry crossover. We discuss these differences as well as implications for properties such as electric polarization in terms of presence or absence of the P–P dimer and other aspects of local structure, sheet density, and size of the van der Waals gap.
36 MATERIALS SCIENCE↗
Automated prediction of lattice parameters from X-ray powder diffraction patterns
A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.
42 ENGINEERING↗