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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 OSTI2025

Poplar: a phylogenomics pipeline

Motivation Generating phylogenomic trees from the genomic data is essential in understanding biological systems. Each step of this complex process has received extensive attention and has been significantly streamlined over the years. Given the public availability of data, obtaining genomes for a wide selection of species is straightforward. However, analyzing that data to generate a phylogenomic tree is a multistep process with legitimate scientific and technical challenges, often requiring a significant input from a domain-area scientist. Results We present Poplar, a new, streamlined computational pipeline, to address the computational logistical issues that arise when constructing the phylogenomic trees. It provides a framework that runs state-of-the-art software for essential steps in the phylogenomic pipeline, beginning from a genome with or without an annotation, and resulting in a species tree. Running Poplar requires no external databases. In the execution, it enables parallelism for execution for clusters and cloud computing. The trees generated by Poplar match closely with state-of-the-art published trees. The usage and performance of Poplar is far simpler and quicker than manually running a phylogenomic pipeline. Availability and implementation Freely available on GitHub at https://github.com/sandialabs/poplar. Implemented using Python and supported on Linux.

Koning, Elizabeth [Sandia National Laboratories (S↗
DOE OSTI2026

Unraveling design principles of protein landscapes in photosynthetic membranes in plant chloroplasts

The supramolecular organization of proteins within photosynthetic membranes is crucial for energy conversion in plants. Here, we introduce an analytical and computational pipeline that integrates high-resolution cryo–scanning electron microscopy, biochemical quantification, advanced Monte Carlo computer simulations, and statistical methods to elucidate the elusive protein landscapes of grana membranes in intact Arabidopsis leaves. Our integrated analysis challenges the prevailing view that particles on the exoplasmic fracture faces in freeze-fracture samples represent photosystem II exclusively. Instead, these particles also include cytochrome b 6 f complexes. Furthermore, our steric clash analysis demonstrates that stacked membranes contain a mixture of larger PSII supercomplexes (C 2 S 2 M 2 and C 2 S 2 ) in addition to a smaller complex (C 2 ). This suggests that in vivo PSII supercomplexes exist in an equilibrium distribution of differing sizes. Furthermore, we discovered that, although size exclusion effects govern the global protein arrangement, local packing exhibits orientational order indicative of lateral attractive protein-protein interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

OES CO 2 Pipeline FEED Project Design Basis Memorandum

The OES CO₂ Pipeline project will move captured carbon dioxide from two ethanol facilities near Gibson City, Illinois, roughly 7.8 miles southeast to three injection wells outside Anchor, where it will be permanently stored underground. The system is designed to handle up to 4.5 million metric tonnes per year of dense-phase CO₂ at pressures up to 2,500 psig, using 16-inch mainline pipe and 10.750-inch laterals made from API 5L X-60 and X-65 steel. Wall thicknesses vary depending on location, with thinner pipe in open country, heavier wall at road crossings, and the heaviest where the pipe passes under highways or railroads via horizontal directional drill. The pipe gets a fusion-bonded epoxy coating, with an added abrasion-resistant layer wherever it's bored or drilled. Major water crossings will use HDD rather than open trenching. The pipeline will be cathodically protected, equipped with SCADA-compatible pressure and temperature instrumentation, and monitored for leaks using a computational pipeline monitoring system per API RP 1130. Hydrostatic testing will be performed at 1.25 times design pressure, and an ILI caliper run will follow to catch any construction defects. Several items, including fracture toughness requirements, specific NDE methods, and ILI tool selection, are left for the detailed design phase. The whole system falls under 49 CFR Part 195 and ASME B31.4, and Gulf Interstate Engineering prepared this document as the FEED-level design basis under the CarbonSAFE Phase III program.

09 BIOMASS FUELS↗
DOE OSTI2021

Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

EvoNet: A phylogenomic and systems biology approach to identify genes underlying plant survival in marginal, low‐N soils

The DOE‐BER “EvoNet” project investigates the genetic and molecular basis of plant resilience in extreme environments. We do this by identifying key genes that enable “extreme survivor” species to thrive in the nitrogen-poor soils of Chile’s hyper-arid Atacama Desert. Our collections focus on 32 Atacama extremophile species, including seven grass species with potential biofuel applications. To identify genes-of-importance to survival we compared genomic and transcriptomic profiles of extremophile species that thrive in the Atacama to those of closely related “sister” species from nitrogen-rich arid and mesic regions of California. Deep RNA sequencing and de novo transcriptome assembly across these triplet species sets supported a phylogenomic framework for identifying positively selected genes associated with adaptive divergence. Our integrative analysis combined ecological and environmental data, metagenomics, evolutionary and systems biology, and metabolomics. This enabled us to create an unprecedented framework for systematically understanding how non-model plants have adapted to survive in extreme conditions. Our resulting database of positively selected ortholog groups in the extremophile plants offers promising targets for engineering crop and biofuel species with enhanced resilience to drought and extreme weather. Additionally, our newest dataset explores and exploits a complementary metabolomic approach. This new aspect provides innovative strategies to manipulate plant cell metabolism, further supporting efforts to improve agricultural productivity in the face of extreme climates. Importantly, our combined evolutionary- and metabolomic-based strategies focused on convergent patterns of adaptation, providing a genetic and metabolomic toolkit for improving crop and biofuel resilience across diverse plant species. Finally, our novel exploration of ecological and evolutionary dynamics delivered to the community a phylogenomic computational pipeline called “PhyloGeneious.” Our continued adaptations of this pipeline are publicly available to expedite evolutionary genomic research for future scientific discoveries. In total, our DOE-BER has provided genomic, metabolomic, and computational strategies to understand how extremophile plants provide evolutionary and physiological targets for improving agricultural and biofuel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗
DOE OSTI2022

A reusable neural network pipeline for unidirectional fiber segmentation

Abstract Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al . To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

In‐Silico Device Performance Prediction of Cosensitizer Dye Pairs for Dye‐Sensitized Solar Cells

Abstract Endeavors in the field of dye‐sensitized solar cells (DSCs) have shown great promise when adopting a data‐driven approach to materials discovery, such as successful molecular‐scale predictions of light‐harvesting chromophores. However, predictions of DSC dyes would become much more sophisticated if a molecular‐to‐macroscopic DSC device prediction methodology existed. Thereby, a fully computational pipeline is presented that predicts device‐performance parameters of DSCs which contain varying dye combinations. Optimal pairing of complementary dyes is identified via a data‐driven workflow that affords cosensitized DSCs with maximum power‐conversion efficiencies. Six high‐performing DSC dyes are paired with partner dyes that are screened from a database of 8488 compounds using sequential heuristic filters. Existing models that predict short‐circuit‐current density ( J SC ) and open‐circuit voltage ( V OC ) parameters are adapted to predict singly sensitized and cosensitized DSC performance. The predictions for J sc values of singly sensitized devices match experimental literature values with comparable accuracy to more computationally costly methods. Five out of six dye pairings are predicted to have greater J SC values when cosensitized compared to their corresponding singly sensitized devices, including two pairs that show strong J sc boosts of +13% and +12% when cosensitized. Thus, the prospect of an entirely in‐silico prediction pipeline for DSC performance that can be used to realize the fully automated design of optimized cosensitized DSCs is demonstrated.

14 SOLAR ENERGY↗
DOE OSTI2020

Machine Learning for Synchrophasor Analysis

The report presents results from the development of a cloud-based, Big Data analysis framework for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Several Machine Learning (ML) methods have been developed and implemented for event and anomaly detection and classification. Actual examples of power system events detection and analysis using synchrophasor data are presented. It has been shown that applications of the cloud-based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Portable Parallel Algorithms and Frameworks for Exascale Graph Analytics

Graphs (or networks) are a tool used to model the interactions among various entities. Efficiently processing large graphs has recently attracted significant attention due to the applications of graphs in various domains, such as biology, chemistry, and cyber-security. Analyzing the structure and properties of these graphs is an important component of many scientific computing pipelines. With the explosion in the volume of data, graphs have become very large and can contain hundreds of billions of vertices and trillions of edges. Therefore, it is crucial to develop high-performance methods to enable graph analysis to be done quickly and energy-efficiently. Furthermore, these solutions should be highly parallel in order to take advantage of modern parallel machines. However, designing efficient solutions is not enough. With the wide variety of computing environments available, each with different programmability and performance characteristics, it is necessary to develop solutions that are portable in terms of both performance (i.e., provide theoretical guarantees) and programmability (i.e., provide high level abstractions).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Tools Assessing Performance (TAP) 2.0

Dmitry Duplyakin will be presenting on the latest research and results in the Tools Assessing Performance (TAP) 2.0 project. This presentation will include updates on the latest data the group has produced, integration of obstacle models in the computational pipeline for distributed wind siting, and the plans for the near-term analysis and validation efforts. The talk will acknowledge the work of collaborators from NREL and three other national labs - ANL, LANL, and PNNL - all contributing to this multi-year project.

distributed wind↗
NASA NTRS1974

Parallel tridiagonal equation solvers

Three parallel algorithms were compared for the direct solution of tridiagonal linear systems of equations. The algorithms are suitable for computers such as ILLIAC 4 and CDC STAR. For array computers similar to ILLIAC 4, cyclic odd-even reduction has the least operation count for highly structured sets of equations, and recursive doubling has the least count for relatively unstructured sets of equations. Since the difference in operation counts for these two algorithms is not substantial, their relative running times may be more related to overhead operations, which are not measured in this paper. The third algorithm, based on Buneman's Poisson solver, has more arithmetic operations than the others, and appears to be the least favorable. For pipeline computers similar to CDC STAR, cyclic odd-even reduction appears to be the most preferable algorithm for all cases.

Stone, H. S.↗
DOE OSTI2024

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict

The reconstruction of complete microbial metabolic pathways using ‘omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

A Tool for Automatic Data Distribution for CFD Applications on Structured Grids

Development of HPF versions of NPB and ARC3D has shown that HPF provides an efficient, concise way to express parallelism and to organize data traffic. The use of HPF, as noted in the papers, requires an intimate knowledge of the applications and a detailed analysis of data affinity, data movement, and data granularity. To simplify and accelerate the task of developing HPF versions of existing CFD applications we have designed and implemented ADAPT (Automatic Data Alignment and Placement Tool). ADAPT analyzes a CFD application working on a single structured grid and generates HPF TEMPLATE, (RE)DISTRIBUTION, ALIGNMENT, and INDEPENDENT directives. The directives can be generated on the nest level, subroutine level, application level, or on the application interface level. ADAPT annotates an existing CFD FORTRAN application, performing computations on single or multiple grids. On each grid the application is considered as a sequence of operators, each applied to a set of variables defined in a particular grid domain. ADAPT automatically detects implicit operators (i.e., having data dependences) and explicit operators (without data dependences). For parallelization of an explicit operator ADAPT creates a template for the operator domain, aligns arrays used in the operator with the template, distributes the template, and declares the loops over the distributed dimensions as INDEPENDENT. For parallelization of an implicit operator, the distribution of the operator's domain should be consistent with the operator's dependences. Any dependence between sections distributed on different processors would preclude parallelization if the compiler does not have an ability to pipeline computations. If a data distribution is "orthogonal" to the dependences of an implicit operator, then the loop which implements the operator can be declared as INDEPENDENT. ADAPT starts with an analysis of array index expressions of the loop nests. For each pair of arrays referenced in an assignment statement, it generates an arc in the alignment graph and annotates it with an affinity relation. The template, alignment, and distribution directives for a particular loop nest are then derived from a transitive closure of the affinity relation. A compromise of data distributions in different nests and subroutines is achieved by merging annotated alignment graphs for adjacent nests/stibroutine calls in the nest/call graph of the application in the process called distribution lifting. ADAPT has been implemented as a C++ program running in conjunction with a parallelization tool called CAPTools. ADAPT uses the parse tree, interprocedural analysis and application database generated by CAPTools. It also uses the Directed Graph class, initially implemented in p2d2 (parallel debugger oi distributed programs), and some other classes supporting symbolic computations. ADAPT uses data distribution techniques described. ADAPT was tested with ARC3D and the FT benchmark and has demonstrated a code performance within a factor of 1.5 of handwritten versions.

Frumkin, Michael↗
NASA NTRS1999

Automatic Data Distribution for CFD Applications on Structured Grids

Development of HPF versions of NPB and ARC3D showed that HPF has potential to be a high level language for parallelization of CFD applications. The use of HPF requires an intimate knowledge of the applications and a detailed analysis of data affinity, data movement and data granularity. Since HPF hides data movement from the user even with this knowledge it is easy to overlook pieces of the code causing low performance of the application. In order to simplify and accelerate the task of developing HPF versions of existing CFD applications we have designed and partially implemented ADAPT (Automatic Data Distribution and Placement Tool). The ADAPT analyzes a CFD application working on a single structured grid and generates HPF TEMPLATE, (RE)DISTRIBUTION, ALIGNMENT and INDEPENDENT directives. The directives can be generated on the nest level, subroutine level, application level or inter application level. ADAPT is designed to annotate existing CFD FORTRAN application performing computations on single or multiple grids. On each grid the application can considered as a sequence of operators each applied to a set of variables defined in a particular grid domain. The operators can be classified as implicit, having data dependences, and explicit, without data dependences. In order to parallelize an explicit operator it is sufficient to create a template for the domain of the operator, align arrays used in the operator with the template, distribute the template, and declare the loops over the distributed dimensions as INDEPENDENT. In order to parallelize an implicit operator, the distribution of the operator's domain should be consistent with the operator's dependences. Any dependence between sections distributed on different processors would preclude parallelization if compiler does not have an ability to pipeline computations. If a data distribution is "orthogonal" to the dependences of an implicit operator then the loop which implements the operator can be declared as INDEPENDENT.

Frumkin, Michael↗
DOE OSTI2024

Heracles: Predictive Tools for Opioid Crisis Intervention - m/q Initiative Project Report

The opioid crisis in the United States is being fueled primarily by fentanyl and its molecular analogs, which can be anywhere from 50 to 1,000 times more potent than morphine. Fentanyl itself is straightforward to synthesize; furthermore, the structure is such that fentanyl’s flexible, rotatable side chains are easy to modify to create new analogs. Reference-free computational techniques to predict and identify new fentanyls have the potential to provide a desperately needed preemptive advantage to regulatory stakeholders and toxicologists. The computational pipeline Heracles was developed with this preemptive advantage in mind. Heracles has two primary components: 1) the creation of an in silico library of putative fentanyl analogs, and 2) a downselection pipeline to prioritize generated fentanyl analogs predicted to be potent and easy to synthesize. Experimental observables were also predicted for prioritized analogs, with validation of the observables begun. Heracles has demonstrated potential to aid in the advancement of reference-free paradigms while providing new tools to first responders and other stakeholders attempting to mitigate the opioid crisis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

CRISPR-COPIES: Web Tool

CRISPR/Cas system has emerged as a powerful genome-editing tool for metabolic engineering and human gene therapy. However, the conundrum of where to integrate heterologous genes on the chromosome using the CRISPR/Cas system remains an open question. Selecting a site for gene integration requires incorporation of complex criteria such as factors involved in CRISPR/Cas-mediated integration, genetic stability, and gene expression and therefore, usually requires strenuous characterization of sites on particular or different chromosomal locations. To address these issues, we developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites. The tool applies ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. This submission contains the code we developed to create a user-friendly web interface for CRISPR-COPIES (https://biofoundry.web.illinois.edu/copies/). We anticipate CRISPR-COPIES will serve as a useful tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, rapid strain construction to produce valuable biochemicals, and human gene and cell therapy.

Bioinformatics↗
DOE OSTI2024

CRISPR-COPIES: an in silico platform for discovery of neutral integration sites for CRISPR/Cas-facilitated gene integration

Abstract The CRISPR/Cas system has emerged as a powerful tool for genome editing in metabolic engineering and human gene therapy. However, locating the optimal site on the chromosome to integrate heterologous genes using the CRISPR/Cas system remains an open question. Selecting a suitable site for gene integration involves considering multiple complex criteria, including factors related to CRISPR/Cas-mediated integration, genetic stability, and gene expression. Consequently, identifying such sites on specific or different chromosomal locations typically requires extensive characterization efforts. To address these challenges, we have developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites. This tool leverages ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search, and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. As a proof of concept, we utilized CRISPR-COPIES to characterize neutral integration sites in three diverse species: Saccharomyces cerevisiae, Cupriavidus necator, and HEK293T cells. In addition, we developed a user-friendly web interface for CRISPR-COPIES (https://biofoundry.web.illinois.edu/copies/). We anticipate that CRISPR-COPIES will serve as a valuable tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, enable rapid strain construction to produce valuable biochemicals, and support human gene and cell therapy applications.

59 BASIC BIOLOGICAL SCIENCES↗