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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Germline-Targeting Chimpanzee SIV Envelope Glycoprotein Elicits a New Class of V2-Apex Directed Cross-Neutralizing Antibodies

HIV-1 and its SIV precursors share a broadly neutralizing antibody (bNAb) epitope in variable loop 2 (V2) at the envelope glycoprotein (Env) trimer apex. Here, we tested the immunogenicity of germ line-targeting versions of a chimpanzee SIV (SIVcpz) Env in human V2-apex bNAb heavy-chain precursor-expressing knock-in mice and as chimeric simian-chimpanzee immunodeficiency viruses (SCIVs) in rhesus macaques (RMs). Trimer immunization of knock-in mice induced V2-directed NAbs, indicating activation of V2-apex bNAb precursor-expressing mouse B cells. SCIV infection of RMs elicited high-titer viremia, potent autologous tier 2 neutralizing antibodies, and rapid sequence escape in the canonical V2-apex epitope. Six of seven animals also developed low-titer heterologous plasma breadth that mapped to the V2-apex. Antibody cloning from two of these animals identified multiple expanded lineages with long heavy chain third complementarity determining regions that cross-neutralized as many as 7 of 19 primary HIV-1 strains, but with low potency. Negative stain electron microscopy (NSEM) of members of the two most cross-reactive lineages confirmed V2 targeting but identified an angle of approach distinct from prototypical V2-apex bNAbs, with antibody binding either requiring or inducing an occluded-open trimer. Probing with conformation-sensitive, nonneutralizing antibodies revealed that SCIV-expressed, but not wild-type SIVcpz Envs, as well as a subset of primary HIV-1 Envs, preferentially adopted a more open trimeric state. These results reveal the existence of a cryptic V2 epitope that is exposed in occluded-open SIVcpz and HIV-1 Env trimers and elicits cross-neutralizing responses of limited breadth and potency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Geometric Interpretation of the Cluster Location Problem Part II: Application to the Pahala, Hawaii, Earthquake Sequence

In the companion “Theory” article, we presented a new framing of the seismic location problem in terms of differential geometry (Harris et al., 2025). From that viewpoint, we developed a “project and correct” approach for estimating the relative locations of earthquakes. Here, in this study, we use project and correct to estimate high-precision relative locations of events from an earthquake sequence beneath the town of Pahala, Hawaii, using high-precision correlation-derived picks. The sequence was active from 2020 through 2022 and produced many highly correlated signals at Hawaii Volcano Observatory (HVO) stations on the island of Hawaii. The data we inverted consisted of 2882 events with observations at 5 HVO stations. For comparison with the travel-time image, we also produced conventional hypocenter solutions using both the Bayesloc program (Myers et al., 2007, 2009) and a purpose-built double-difference code. There were obvious structural elements in the resulting image, the resolution of which we used to test the performance of the project and the correct algorithm. For the projection step, we first produced a 3D local basis using an singular value decomposition (SVD) of the 2882 groups of times. Projection of the travel-time vectors into this basis resulted in an image with structures similar to those produced by our conventional locators, but with distortion as predicted by theory. Removing the distortion requires an inverse operator generated from the metric tensor at the geometric centroid of the events. We compared two approaches to obtaining such an inverse operator. The first uses an estimate of the geographic centroid of the event cloud from the centroid of the travel-time data. The second approach uses the centroid of the conventionally produced locations. The first approach produces a corrected image very similar to the conventional results, but with a rotation. The corrected image produced using the conventionally derived centroid is a near-exact match to the conventional locations.

Dodge, Douglas A. [Lawrence Livermore National Lab↗
DOE OSTI2025

Analysis of Warped April Tag Impacts on Detection and Pose Estimation

This report evaluates the impact of geometric deformation on an April Tag, particularly when warped due to attachment on a curved surface, on its detectability and pose estimation performance. A comparative analysis was conducted using a flat April Tag as a control under identical experimental conditions, which involved recording video sequences with varying viewing angles. For detectability, the warped tag exhibited consistent detection failures at viewing angles beyond 40° and complete failures beyond 60°, whereas the flat tag maintained reliable detection across all angles. For pose estimation, measured by pose jitter (variation in rotation and translation), differences between the warped and flat tags were minimal and statistically insignificant, indicating robust performance even for the deformed tag. These findings suggest that while geometric warping reduces an April Tag’s detectability, its pose estimation accuracy remains relatively unaffected under the tested conditions.

42 ENGINEERING↗
DOE OSTI2021

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Stability Analysis Tool for Bulk Power Systems Using Black-Box Models of Inverter-Based Resources

This paper presents a small-signal stability analysis tool for large-scale power systems with high penetration of inverter-based resources (IBRs). Firstly, a network transfer function matrix (NTFM), which represents the information of the system topology, transmission lines, loads, IBRs locations, etc., is derived to model the entire power system network. Secondly, small-signal perturbation method is applied to obtain the sequence impedance/admittance models of the block-box IBRs considering the frequency cross-coupling effects. With the obtained NTFM as well as IBR models, a multi-input, multi-output (MIMO) feedback system is constructed, and the generalized Nyquist criterion (GNC)-based stability method is employed to analyze the stability of the entire power system. Furthermore, based on the developed stability analysis method, sensitivity analysis is conducted on an unstable case to identify which parameter has a high impact on the system stability. As a result, different test cases based on a modified IEEE 14- bus system as well as a reduced 240-bus WECC system are studied to verify the proposed stability analysis tool.

42 ENGINEERING↗
DOE OSTI2020

Predicting antimicrobial susceptibility from the bacterial genome: A new paradigm for one health resistance monitoring

The laboratory identification of antibacterial resistance is a cornerstone of infectious disease medicine. In vitro antimicrobial susceptibility testing has long been based on the growth response of organisms in pure culture to a defined concentration of antimicrobial agents. By comparing individual isolates to wild type susceptibility patterns, strains with acquired resistance can be identified. Acquired resistance can also be detected genetically. After many decades of research, the inventory of genes underlying antimicrobial resistance (AMR) is well known for several pathogenic genera including zoonotic enteric organisms such as Salmonella and Campylobacter and continues to grow substantially for others. With the decline in costs for large scale DNA sequencing, it is now practicable to characterize bacteria using whole genome sequencing (WGS), including the carriage of resistance genes in individual microorganisms and those present in complex biological samples. With genomics we can generate comprehensive, detailed information on the bacterium, the mechanisms of antibiotic resistance, clues to its source, and the nature of mobile DNA elements by which resistance spreads. These developments point to a new paradigm for antimicrobial resistance detection and tracking for both clinical and public health purposes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Radioimaging for real-time tracking of high-voltage breakdown

Development of a radioimaging diagnostic for high-voltage component reliability testing and electrical breakdown computational model validation is described. Radioimaging has its roots in radio astronomy, where aperture synthesis (also known as synthesis imaging) has been utilized for decades to image radio sources far from Earth. Radioimaging as described herein, in contrast, seeks to image radio sources in close proximity to its receivers (i.e., in a laboratory environment). Here it is shown that corona discharge, a non-destructive precursor to catastrophic (thermal) arc discharge, electromagnetically radiates strongly within a 250 kHz – 2.5 GHz bandwidth, and is readily detected and located by postprocessing the received radio signals. The ability of radioimaging to detect both corona and arc discharge (grouped together herein as high voltage breakdown or HVB) makes it a valuable tool for 100% HVB detection in materials, components, and devices, and has the ability to indicate electrical weakness (via corona detection) prior to a destructive arc discharge event. Radioimaging enables HVB to be located both internal and external to dielectric components under test in near-real-time, with multiple and/or extended HVB events located simultaneously. In contrast, existing non-destructive diagnostics (at the time of this writing) either indicate electrical breakdown without resolving failure locations (e.g., current, voltage, and chemical measurements), locate external HVB (e.g., high-speed optical and ultraviolet (UV) measurements or photography), or locate both external and internal HVB but with low fidelity (e.g., a single HVB source can be located by existing time-of-arrival (TOA) UHF or acoustic emissions). Radioimaging instead creates a sequence of high-fidelity images similar to an optical high-speed camera but at radiofrequencies (RF), and is not limited to two-dimensions. Moreover, radioimaging has already served one internal and two external industry customers, the results of which are detailed in this report. The radioimaging results described herein were part of a three-year effort funded by the Sandia Lab Directed Research and Development (LDRD) program within the Radiation, Electromagnetic, High Energy Density Science (REHEDS) investment area.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Contrasting decays of 3-quasiparticle isomers in 173 W: A discerning test of 𝐾 conservation and 𝐾 mixing

Two isomers with K π = 19/2 + at 1876 keV with a half-life of 21(1) ns, and K π = 23/2− at 2159 keV with a half-life of 93(6) ns have been identified in 173 W, the lightest tungsten isotope to exhibit this phenomenon. An unusually high number of 13 branches have been established in the decay of the K π = 23/2 − isomer. This decay is associated with transitions involving significantly higher degrees of K forbiddenness and is in sharp contrast with the usual preferential deexcitation through branches, which are less forbidden, as is evident for the K π = 19/2 + state in 173 W. The inclusion of K-mixing effects, arising from band crossings in the sequences to which decays with larger degrees of K forbiddenness are observed, can qualitatively account for this contrasting behavior. These results provide a striking illustration of the competition between K conservation and K mixing in the decay of such isomers.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A Detailed Reaction Mechanism for Thiosulfate Oxidation by Ozone in Aqueous Environments

The ozone oxidation, or ozonation, of thiosulfate is an important reaction for wastewater processing, where it is used for remediation of mining effluents, and for studying aerosol chemistry, where its fast reaction rate makes it an excellent model reaction. Although thiosulfate ozonation has been studied since the 1950s, challenges remain in developing a realistic reaction mechanism that can satisfactorily account for all observed products with a sequence of elementary reaction steps. Here, we present novel measurements using trapped microdroplets to study the pH-dependent thiosulfate ozonation kinetics. We detect known products and intermediates, including SO32-, SO42-, S3O62-, and S4O62-, establishing agreement with the literature. However, we identify S2O42- as a new reaction intermediate and find that the currently accepted mechanism does not directly explain observed pH effects. Thus, we develop a new mechanism, which incorporates S2O42- as an intermediate and uses elementary steps to explain the pH dependence of thiosulfate ozonation. The proposed mechanism is tested using a kinetic model benchmarked to the experiments presented here, then compared to literature data. We demonstrate good agreement between the proposed thiosulfate ozonation mechanism and experiments, suggesting that the insights in this paper can be leveraged in wastewater treatment and in understanding potential climate impacts.

Deal, Alexandra M↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .

Marfo, William [Univ. of Texas, El Paso, TX (Unite↗
DOE OSTI2020

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. Although it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. Furthermore, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Advances in genetic tools for metabolic engineering of non-conventional yeasts

Non-conventional yeasts are emerging as powerful alternatives to Saccharomyces cerevisiae for metabolic engineering, owing to their innate stress tolerance, broad substrate utilization, and distinctive metabolic capabilities. These attributes position them as promising chassis for producing biofuels, pharmaceuticals, and specialty chemicals. This review synthesizes recent advances in genetic toolkits for four such species—Pichia kudriavzevii (Issatchenkia orientalis), Starmerella bombicola, Debaryomyces hansenii, and Pachysolen tannophilus—highlighting progress across plasmid architectures (episomal and integrative), identification of autonomously replicating sequences and centromeric elements, and the development of safe-harbor genomic loci. We summarize promoter and terminator libraries enabling tunable expression, the expansion of auxotrophic and antifungal selection markers with recycling strategies, and the rapid adaptation of CRISPR-based systems (Cas9 and Cas12a) with optimized guide RNA expression, multiplex editing, and approaches that enhance homologous recombination (e.g., KU70/80 disruption). We also review landing-pad platforms for modular, repeated integrations and transposon-based tools (e.g., piggyBac) that facilitate multigene pathway assembly. Collectively, these innovations are accelerating design-build-test-learn cycles and enabling precise, scalable engineering of non-conventional yeasts. Remaining challenges—including limited species-specific episomal systems, variable transformation efficiencies, genome-stability concerns, and alternative codon usage—define clear priorities for future toolkit development. Together, these advances and open needs chart a path toward robust, sustainable biomanufacturing using diverse non-conventional yeast chassis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 1. Evaluating Above- and Below-ground Controls of Flow Persistence in a Forested Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in a forested catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, ground penetrating radar (GPR), continuous self-potential (SP) monitoring, electromagnetic (EM) imaging, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Contains two subfolders: Synthetic and Field_Application subfolder. Synthetic subfolder contains the ATS XML input script (can be opened using any code editor) for the four synthetic hydrological cases tested (Connected and gaining, Connected and losing, Disconnected and losing, and dry stream). It also includes other experimental cases to test the influence of precipitation and concentration gradient. For each synthetic case, the flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.mph can be opened with the commercial software COMSOL and requires a license) is executed using the ATS output data to simulate the potential field. It also includes the Synthetic_model_plot.ipynb (can be opened using any code editor) to visualize the SP result and generate manuscript figures. The data subfolder contains mesh files to run both the ATS (.exo and .stl files can be viewed using Paraview; .h5 files can be opened using HDFView software and h5py Python package) and COMSOL models. Field_Application subfolder contains two subfolders: ES_MDA_inversion and Final_Model. ES_MDA_inversion contains the Python script (.py can be opened using any code editor) and SP observation data used to run the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) inversion sequence to get the optimal model parameters. The Final_model subfolder contains the ATS XML input scripts, data files, output data for the two SP sites. The same workflow steps outlined for the Synthetic subfolder apply here. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) EM Contains the CSV file of the EM data from the DUALEM-42, including spatial coordinates (x, y, z), apparent conductivity, and in-phase measurements at 2 m coil separations for horizontal coplanar (HCP) and perpendicular (PRP) geometries. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion (.resipy can be opened with the open-source ResIPy software). 6) GPR Includes GPR field datasets collected at 100 MHz and 250 MHz antenna frequencies, along with the processing/interpretation project file (GPR_process.gpz can be viewed using EKKO_Project 6, a commercial software by Sensors & Software that requires a license). 7) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 8) SP Contains the SP data collected in field at the two SP sites (one in the perennial reach and the other in the intermittent reach), provided as DAT files. 9) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). It also includes DTW.ipynb, a Jupyter notebook containing the code for the dynamic time warping (DTW) with sliding window to evaluate SP signal synchronicity.

ATS↗
DOE OSTI2020

Preliminary Study of Potential Utilization of Triassic Rift Basins for Long-Term Carbon Sequestration

Triassic rift basins of the eastern United States present a potential option for long–term carbon dioxide (CO 2 ) sequestration. Exposed and buried basins are located near large point CO 2 sources. Because of their similar origins, comparable fill successions indicate a level of reproducibility that can be conveyed between basins. The thick rock successions of the exposed Culpeper and Gettysburg basins served as proxies for understanding of the basin sequences. Their study allowed recognition of five mappable assemblages of rock types, herein termed lithofacies associations. These lithofacies associations were formed by alluvial fan, braided and meandering streams and marginal and distal lake depositional processes.Stratigraphic architecture of the lithofacies associations within exposed basins suggests a vertical succession that consists of an initial coarse-grained fluvial succession that is progressively replaced upward and basinward by finer grained lacustrine deposits. Along the faulted margins of the basins alluvial fans transitioning to fluvial delta deposits reflect lateral progradation and contemporaneous lateral components of the basin filling.To test the model devised in exposed basins, facies associations were developed for the buried Taylorsville Basin. This basin preserves over 8,000 feet of Triassic rocks that are concealed beneath more than 2,000 feet of Cretaceous and Tertiary Coastal Plain sediments. Composition and stratigraphic architecture of these rocks are similar to those observed in exposed basins. Infilling of the basin was the result of vertical aggradation and lateral progradation of coarse-grained to fine-grained facies. Based upon study of outcrops in the Culpeper and Gettysburg basins, groups of recurring lithologic facies were identified as the fundamental constructs of the basin’s sediment infilling. These groups of facies, termed lithofacies associations, represent an amalgamation of lithologic components from broadly similar depositional systems. These lithologic associations were then extrapolated into the exposed and buried portions of the Taylorsville basins. This effort determined that Triassic rift basins provide several avenues for potential study of geologic sequestration of carbon. The characteristic rift basin succession in the basins presents possible reservoir targets within the marginal fluvial deposits. Furthermore, intrusive and extrusive mafic bodies present a potential source of fracture porosity that could serve as CO 2 reservoirs. Porosity values from thin section analysis of exposed basin samples are higher in alluvial fan and braided fluvial lithofacies. Porosity is highest in samples that show less compaction, due to a lack of ductile lithic fragments and/or higher stratigraphic position. Log data from the Taylorsville Basin indicates that porosity and permeability values in basin marginal fluvial strata are elevated. Near the center of the basin, porosity and permeability values are greatly reduced, primarily owing to the fine-grained character of the lacustrine deposits. Some Triassic basins also contain thick intervals of concordant extrusive and intrusive mafic igneous rocks. These igneous bodies are potential CO 2 reservoirs for several reasons. Firstly, extrusive lava flows provide potential storage in the layers of primary porosity that occur in the vesicules formed at the top of lava flows. Secondly, both lava flows and subsurface igneous sills exhibit extensive fracture porosity produced by the rapid cooling of the flows and intrusions. Thirdly, these igneous rocks are mafic in composition, and studies have shown that iron- and magnesium-rich mafic rocks provide sequestration opportunities through carbonate remineralization. Lastly, extrusive and intrusive igneous rocks are invariably preserved within fine-grained lake deposits. These lacustrine sediments can serve as a fine-grained confining layer that encases the igneous rocks both above and below.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Physics-informed neural networks (PINNs) have shown to be effective tools for solving both forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network using automatic differentiation, and this PDE loss is evaluated at a set of scattered spatio-temporal points (called residual points). The location and distribution of these residual points are highly important to the performance of PINNs. However, in the existing studies on PINNs, only a few simple residual point sampling methods have mainly been used. Here, we present a comprehensive study of two categories of sampling for PINNs: non-adaptive uniform sampling and adaptive nonuniform sampling. We consider six uniform sampling methods, including (1) equispaced uniform grid, (2) uniformly random sampling, (3) Latin hypercube sampling, (4) Halton sequence, (5) Hammersley sequence, and (6) Sobol sequence. We also consider a resampling strategy for uniform sampling. To improve the sampling efficiency and the accuracy of PINNs, we propose two new residual-based adaptive sampling methods: residual-based adaptive distribution (RAD) and residual-based adaptive refinement with distribution (RAR-D), which dynamically improve the distribution of residual points based on the PDE residuals during training. Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling. We extensively tested the performance of these sampling methods for four forward problems and two inverse problems in many setups. Our numerical results presented in this study are summarized from more than 6000 simulations of PINNs. Here, we show that the proposed adaptive sampling methods of RAD and RAR-D significantly improve the accuracy of PINNs with fewer residual points for both forward and inverse problems. Furthermore, the results obtained in this study can also be used as a practical guideline in choosing sampling methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Pareto Optimization of Oligomer Polarizability and Dipole Moment Using a Genetic Algorithm

High-performance electronic components are highly sought after in order to produce increasingly smaller and cheaper electronic devices. Drawing inspiration from inorganic dielectric materials, in which both polarizability and polarization contribute, organic materials can also maximize both. For a large set of small molecules drawn from PubChem, a Pareto-like front appears between the polarizability and dipole moment, indicating the presence of an apparent trade-off between these two properties. We tested this balance in π-conjugated materials by searching for novel conjugated hexamers with simultaneously large polar- izabilities and dipole moments with potential use for dielectric materials. Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight-binding method for property calculations, we were able to efficiently search chemical space for optimal hexamers. Given the scope of chemical space, using the GA technique saves considerable time and resources by speeding up molecular searches compared to a systematic search. Here, we also explored the underlying structure–function relationships, including sequence and monomer properties, that characterize large polarizability and dipole moment regimes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗