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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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At least 127 records · Page 7

Regioselectivity mechanism of the Thunbergia alata Δ 6 -16:0-acyl carrier protein desaturase

Plant plastidial acyl–acyl carrier protein (ACP) desaturases are a soluble class of diiron-containing enzymes that are distinct from the diiron-containing integral membrane desaturases found in plants and other organisms. The archetype of this class is the stearoyl-ACP desaturase which converts stearoyl-ACP into oleoyl (18:1Δ 9 cis )-ACP. Several variants expressing distinct regioselectivity have been described including a Δ 6 -16:0-ACP desaturase from black-eyed Susan vine ( Thunbergia alata ). We solved a crystal structure of the T. alata desaturase at 2.05 Å resolution. Using molecular dynamics (MD) simulations, we identified a low-energy complex between 16:0-ACP and the desaturase that would position C6 and C7 of the acyl chain adjacent to the diiron active site. The model complex was used to identify mutant variants that could convert the T. alata Δ 6 desaturase to Δ 9 regioselectivity. Additional modeling between ACP and the mutant variants confirmed the predicted regioselectivity. Furthermore, to validate the in-silico predictions, we synthesized two variants of the T. alata desaturase and analyzed their reaction products using gas chromatography-coupled mass spectrometry. Assay results confirmed that mutants designed to convert T. alata Δ 6 to Δ 9 selectivity exhibited the predicted changes. In complementary experiments, variants of the castor desaturase designed to convert Δ 9 to Δ 6 selectivity lost some of their Δ9 desaturation ability and gained the ability to desaturate at the Δ 6 position. The computational workflow for revealing the mechanistic understanding of regioselectivity presented herein lays a foundation for designing acyl-ACP desaturases with novel selectivities to increase the diversity of monoenes available for bioproduct applications.

59 BASIC BIOLOGICAL SCIENCES↗

Orientation-dependent properties of epitaxially strained perovskite oxide thin films: Insights from first-principles calculations

The structural properties, energetics, and polarizations of perovskite-based thin-film oxide systems are computed as a function of biaxial strain state and epitaxial orientation, employing an automated computational workflow based on density functional theory. In this work, a total of 14 compositions are considered, of the form ABO 3 , with A = Ba, K, Na, Pb, and Sr and B = Hf, Sn, Ti, Zr, Nb, Ta, and V site cations chosen to yield tolerance factors with values ranging between 0.95 and 1.1. Three biaxial strain states corresponding to epitaxial growth of (100)-, (110)-, and (111)-oriented films are considered, with misfit strains ranging between -4% to 4%. Results are presented for the series of perovskite-derived phases, and their corresponding symmetries, which are energetically favorable as a function of misfit strain, along with their corresponding equilibrium atomic positions, lattice parameters, and electric polarizations. The results demonstrate robust trends of in-plane polarization enhancement under tensile strain for all epitaxial orientations, and out-of-plane polarization enhancementwith compression for the (100)- and (110)-oriented films. Strains corresponding to the (111)-growth orientation lead to a wider variety of out-of-plane polarization behavior, with BaTiO 3 showing anomalous diminishing polarization with compression. Epitaxial orientation is shown to have a strong effect on the nature of strain-induced phase transitions, with (100)-oriented systems tending to have smooth, second-order transitions and (110)- and (111)-oriented systems more commonly exhibiting first-order transitions. The significance of this effect for device applications is discussed, and a number of systems are identified as potentially interesting for ferroelectric thin-film applications based on energetic stability and polarization behavior. Analysis of polarization behavior across different orientations reveals distinct groups into which compositions can be organized, some of which have polarization dependencies on misfit strain that have not been reported previously.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Improving the efficiency of Rubisco by resurrecting its ancestors in the family Solanaceae

Plants and photosynthetic organisms have a remarkably inefficient enzyme named Rubisco that fixes atmospheric CO 2 into organic compounds. Understanding how Rubisco has evolved in response to past climate change is important for attempts to adjust plants to future conditions. In this study, we developed a computational workflow to assemble de novo both large and small subunits of Rubisco enzymes from transcriptomics data. Next, we predicted sequences for ancestral Rubiscos of the (nightshade) family Solanaceae and characterized their kinetics after coexpressing them inEscherichia coli. Predicted ancestors of C 3 Rubiscos were identified that have superior kinetics and excellent potential to help plants adapt to anthropogenic climate change. Our findings also advance understanding of the evolution of Rubisco’s catalytic traits.

59 BASIC BIOLOGICAL SCIENCES↗

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Kulyashov, M. A.↗

Tardigrade-examples V0.1.0

Tardigrade-examples is a repository of computational workflows that exercise the Tardigrade software package. The Tardigrade software package is an implementation of Eringen’s micromorphic continuum theory with capabilities to support multiscale material modeling. These capabilities include homogenization through the Micromorphic Filter, calibration of micromorphic material models, and macroscale simulation in Tardigrade-MOOSE. This repository investigates continuum upscaling of various direct numerical simulations (DNS) conducted in Abaqus finite element (FE), Ratel FE, and GEOS material point method (MPM) software. Verification of the upscaling workflow is first investigated by considering DNS of trivial stress states for homogeneous materials, results of which indicate that classical continuum behavior is recovered as expected. DNS of heterogeneous materials are then considered.

Allard, Thomas↗

Tardigrade-examples V0.2.0

Tardigrade-examples (LANL code O4735) is a repository of computational workflows that exercise the Tardigrade software package.

Allard, Thomas [Los Alamos National Laboratory]↗

Quantum defects in silicon

The dataset used in the publication titled “High-throughput identification of spin-photon interfaces in silicon. Using an automated first-principles computational workflow, we construct a dataset that details simple charged defects in silicon that are relevant for spin-photon interface applications. It encompasses properties vital for quantum defect design, including charged defect stability, excitation energy (determined using the single-shot hybrid functional approach), emission brightness, electronic structures, and more. The dataset encompasses all charged defects stable within the energy gap. We emphasized the importance of defect-bound excitons as telecom band emitters and proposed promising candidates for spin-photon interfaces.

36 MATERIALS SCIENCE↗

Generic Crystalline Disposal Reference Case

A generic reference case for disposal of spent nuclear fuel and high-level radioactive waste in crystalline rock is outlined. The generic cases are intended to support development of disposal system modeling capability by establishing relevant baseline conditions and parameters. Establishment of a generic reference case requires that the emplacement concept, waste inventory, waste form, waste package, backfill/buffer properties, EBS failure scenarios, host rock properties, and biosphere be specified. The focus in this report is on those elements that are unique to crystalline disposal, especially the geosphere representation. Three emplacement concepts are suggested for further analyses: a waste packages containing 4 PWR assemblies emplaced in boreholes in the floors of tunnels (KBS-3 concept), a 12-assembly waste package emplaced in tunnels, and a 32-assembly dual purpose canister emplaced in tunnels. In addition, three failure scenarios were suggested for future use: a nominal scenario involving corrosion of the waste package in the tunnel emplacement concepts, a manufacturing defect scenario applicable to the KBS-3 concept, and a disruptive glaciation scenario applicable to both emplacement concepts. The computational approaches required to analyze EBS failure and transport processes in a crystalline rock repository are similar to those of argillite/shale, with the most significant difference being that the EBS in a crystalline rock repository will likely experience highly heterogeneous flow rates, which should be represented in the model. The computational approaches required to analyze radionuclide transport in the natural system are very different because of the highly channelized nature of fracture flow. Computational workflows tailored to crystalline rock based on discrete transport pathways extracted from discrete fracture network models are recommended.

Environmental Protection↗

Uncovering novel RNA viruses in permafrost

Arctic soils (including permafrost) contain an untapped reservoir of microbial life, including viruses, that are trapped in a frozen state of low metabolic activity. However, as permafrost thaws, they can be revived and pose potential risks to human health and ecosystem stability. The current COVID-19 pandemic has highlighted the urgency of identifying potential pathogen reservoirs which may be activated under the impacts of climate change, as well as understanding the role of RNA viruses in dynamic environments. Here, we applied a new computational workflow (Wu et al. submitted) to identify and analyze RNA viruses from de-novo assemblies representing a total of 33 metatranscriptomes collected from four parallel transects across a range of fluctuating environmental gradients. This study provides the first characterization of RNA viral diversity within a permafrost ecosystem.

58 GEOSCIENCES↗

Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments

Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments

Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Modeling the Spatio-Temporal Variability in Subsurface Thermal Regimes Across a Low-Relief Polygonal Tundra Landscape: Modeling Archive

Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to atmosphere under warming climate. Ice--wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. The microtopography plays a critical role in regulating the fine scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behaviour under current as well as changing climate. We present here an end-to-end effort for high resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites at Barrow, Alaska spanning across low to transitional to high-centered polygon and representative of broad polygonal tundra landscape. A multi--phase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using high resolution LiDAR DEM, microtopographic features of the landscape were characterized and represented in the high resolution model mesh. Best available soil data from field observations and literature was utilized to represent the complex heterogeneous subsurface in the numerical model. This data collection provides the complete set of input files, forcing data sets and computational meshes for simulations using PFLOTRAN for four sites at Barrow Environmental Observatory. It also documents the complete computational workflow for this modeling study to allow verification, reproducibility and follow up studies. This dataset includes two zipped files and one .pdf file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Tutorial for Non-Orthogonal Configuration Interaction Fragment Studies Using GronOR

A brief tutorial is presented for the GronOR non-orthogonal configuration interaction application illustrating the download and build process, setting up calculations with OpenMolcas and GronOR, and a description of the file structure and input options. An overview of the computational workflow includes the generation of fragment wave functions and integrals with an adapted version of OpenMolcas, running GronOR calculations, and accounting for dynamic correlation corrections. Three end-to-end use cases are provided, representing a molecular dimer, an ion pair, and overlapping molecular fragments.

Straatsma, T.P.↗

Treyson Ricks - Intern Showcase Poster

Quinone-based sorbents offer a tunable, energy-efficient route to electrochemical CO2 capture, but systematic guidance for molecular design is lacking. Here, we report a high-throughput computational workflow that combines density functional theory (DFT) screening with machine-learning (ML) modeling to evaluate CO2 binding thermodynamics across several quinone derivatives, spanning benzoquinones, naphthoquinones, and anthraquinones. In addition to using solvents to stabilize the quinone anion and dianion, we studied the effect of ion-pairing on the reduction potentials and the CO2 binding energy. Automated Python scripts handled geometry optimizations and adduct-formation energies on an HPC cluster, reducing manual effort significantly. This integrated platform can uncover structure–property relationships and enables rapid in silico evaluation of untested candidates. We present one example from our workflow to showcase the capability of using quinones with ion-pairing to effectively capture CO2. Our approach paves the way for the rational selection of optimal quinone sorbents and can be extended with experimental thermochemical and kinetic data, alternative redox cycles, and stability assessments to accelerate development of next-generation electrochemical CO2 capture materials.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗

The future of scientific workflows

Today’s computational, experimental, and observational sciences rely on computations that involve many related tasks. The success of a scientific mission often hinges on the computer automation of these workflows. In April 2015, the US Department of Energy (DOE) invited a diverse group of domain and computer scientists from national laboratories supported by the Office of Science, the National Nuclear Security Administration, from industry, and from academia to review the workflow requirements of DOE’s science and national security missions, to assess the current state of the art in science workflows, to understand the impact of emerging extreme-scale computing systems on those workflows, and to develop requirements for automated workflow management in future and existing environments. Here, this paper is a summary of the opinions of over 50 leading researchers attending this workshop. We highlight use cases, computing systems, workflow needs and conclude by summarizing the remaining challenges this community sees that inhibit large-scale scientific workflows from becoming a mainstream tool for extreme-scale science.

97 MATHEMATICS AND COMPUTING↗

Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments

The annual Workshop on Workflows in Support of Large-Scale Science (WORKS) is a premier venue for the scientific workflow community to present the latest advances in research and development on the many facets of scientific workflows throughout their life-cycle. The Lightning Talks at WORKS focus on describing a novel tool, scientific workflow, or concept, which are work-in-progress and address emerging technologies and frameworks to foster discussion in the community. This paper summarizes the lightning talks at the 2023 edition of WORKS, covering five topics: leveraging large language models to build and execute workflows; developing a common workflow scheduler interface; scaling uncertainty workflow applications on exascale computing systems; evaluating a transcriptomics workflow for cloud vs. HPC systems; and best practices in migrating legacy workflows to workflow management systems.

Titov, Mikhail↗

Mesh Computing Remote Automatic Workflow

The software suite uses a microservice architecture using Docker and `docker-compose`. The microservices are as follows: 1. User interface. This interface is written in JavaScript using the Svelte framework. It exposes form elements and a 3D visualizer to prompt the user through the definition of microstructure parameters, and setting parameters for mesh generation and refinement. 2. Mesh generator. This is a container running the Python package for DREAM3D to generate a voxelized mesh that represents a microstructure defined by the user in the interface. 3. Cubit runner. This is a secure shell protocol tool that makes the submitting the DREAM mesh to an HPC instance and starts to run Cubit shell commands to smooth the grain boundaries with its `sculpt` library, applies user-defined boundary node sets, and bundles and returns the simulation-ready meshes and input files as a zipped directory.

Harris, BrennanKay↗