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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Solution scattering at the Life Science X-ray Scattering (LiX) beamline

This work reports the instrumentation and software implementation at the Life Science X-ray Scattering (LiX) beamline at NSLS-II in support of biomolecular solution scattering. For automated static measurements, samples are stored in PCR tubes and grouped in 18-position sample holders. Unattended operations are enabled using a six-axis robot that exchanges sample holders between a storage box and a sample handler, transporting samples from the PCR tubes to the X-ray beam for scattering measurements. The storage box has a capacity of 20 sample holders. At full capacity, the measurements on all samples last for ∼9 h. For in-line size-exclusion chromatography, the beamline-control software coordinates with a commercial high-performance liquid chromatography (HPLC) system to measure multiple samples in batch mode. The beamline can switch between static and HPLC measurements instantaneously. In all measurements, the scattering data span a wide q -range of typically 0.006–3.2 Å −1 . Functionalities in the Python package py4xs have been developed to support automated data processing, including azimuthal averaging, merging data from multiple detectors, buffer scattering subtraction, data storage in HDF5 format and exporting the final data in a three-column text format that is acceptable by most data analysis tools. These functionalities have been integrated into graphical user interfaces that run in Jupyter notebooks, with hooks for external data analysis software.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

BioHackathon 2015: Semantics of data for life sciences and reproducible research

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Scanning structural mapping at the Life Science X-ray Scattering Beamline

This work describes the instrumentation and software for microbeam scattering and structural mapping at the Life Science X-ray Scattering (LiX) beamline at NSLS-II. Using a two-stage focusing scheme, an adjustable beam size between a few micrometres and a fraction of a millimetre is produced at the sample position. Scattering data at small and wide angles are collected simultaneously on multiple Pilatus detectors. A recent addition of an in-vacuum Pilatus 900k detector, with the detector modules arranged in a C-shaped configuration, has improved the azimuthal angle coverage in the wide-angle data. As an option, fluorescence data can be collected simultaneously. Fly scans have been implemented to minimize the time interval between scattering patterns and to avoid unnecessary radiation damage to the sample. For weakly scattering samples, an in-vacuum sample environment has been developed here to minimize background scattering. Data processing for these measurements is highly sample-specific. To establish a generalized data process workflow, first the data are reduced to reciprocal coordinates at the time of data collection. The users can then quantify features of their choosing from these intermediate data and construct structural maps. As examples, results from in-vacuum mapping of onion epidermal cell walls and 2D tomographic sectioning of an intact poplar stem are presented.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Flow matching meets biology and life science: a survey

Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological research and discovery, enabling breakthroughs in molecule design, protein generation, catalysis discovery, drug discovery, and beyond. At the same time, biological applications have served as valuable testbeds for evaluating the capabilities of generative models. Recently, flow matching has emerged as a powerful and efficient alternative to diffusion-based generative modeling, with growing interest in its application to problems in biology and life sciences. This paper presents the first comprehensive survey of recent developments in flow matching and its applications in biological domains. We begin by systematically reviewing the foundations and variants of flow matching, and then categorize its applications into three major areas: biological sequence modeling, molecule generation and design, and peptide and protein generation. For each, we provide an in-depth review of recent progress. We also summarize commonly used datasets and software tools, and conclude with a discussion of potential future directions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Embedding Methods for Quantum Chemistry: Applications from Materials to Life Sciences

Quantum mechanical embedding methods hold the promise to transform not just the way calculations are performed, but to significantly reduce computational costs and improve scaling for macro molecular systems containing hundreds if not thousands of atoms. The field of embedding has grown increasingly broad with many approaches of different intersecting flavors. Here, we lay out the methods into two streams: QM:MM and QM:QM, showcasing the advantages and disadvantages of both. We provide a review of the literature, the underpinning theories including our contributions, and we highlight current applications with select examples spanning both materials and life sciences. Lastly, we conclude with prospects and future outlook on embedding, and our view on the use of universal test case scenarios for cross comparisons of the many available (and future) embedding theories.

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

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

Abstract Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

National Security Life Sciences Overview [Slides]

Establish science-based models and systems of human-environment interactions representing natural threats and anthropogenic non-nuclear threats that inform national policy and decision makers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Opportunities for retrieval and tool augmented large language models in scientific facilities

Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Alvin Tollestrup (1924-2020): A Life in Science

Alvin Tollestrup was a physicist and engineer of uncommon insight, intuition, and ingenuity who was revered in life for his essential leadership in the development of superconducting magnets for high-energy accelerators. He exhibited a matchless mastery of what he called freshman physics — though his mastery hardly ended there — and an uncanny gift for joining careful measurements with transparent calculations to reveal the essence of a subject. To watch Alvin at work was to learn how a true scientist formulates and solves a problem. He had other gifts: a nose for significant problems in both science and technology, the ability to draw talented people to an important cause, and a mentor's instinct for spotting gifted junior colleagues and helping them to mature as engineers and scientists. He was also good company! Alvin brought his keen vision about what constitutes important science, along with powerful ambition for the institution and for fundamental science, to considerat ions about Fermilab's future. His questions were uniformly probing, his opinions unfailingly interesting, and his appetite for work consistently inspiring. Among Alvin's many contributions to experimental particle physics — in a career that spanned seven decades — the rich achievements of the Collider Detector at Fermilab Collaboration, of which he was a founder and an enduring leader, stand out. The discovery of the top quark in 1995 earned worldwide acclaim. He reaped individual accolades as well, including election to the National Academy of Sciences. The National Medal of Technology, conferred in 1989, recognized Alvin’s leading role in developing superconducting magnets for Fermilab's Tevatron. Universities Research Association created the Tollestrup Award for Postdoctoral Research, commemorating Alvin's avuncular support for young scientists. This volume celebrates Alvin Tollestrup’s career and the rich, fulfilling life he led beyond the laboratory. Contributions from colleagues and family illuminate many facets of this remarka! ble figure. May old friends savor the memory of the Alvin they knew, while discovering less-familiar parts of his history; and may readers who did not have the opportunity to know Alvin firsthand find inspiration in his exemplary life in science.

Palmer, Mark↗
DOE OSTI2025

Nanodiamonds in Advancing Biomedical Sciences

Nanodiamonds (NDs), tetrahedral carbon frameworks with size ranging from 1 to 100 nanometers, have gained growing attention in recent years due to their distinct optical, thermal, and mechanical properties compared to other carbon nanomaterials (e.g., graphene, carbon nanotubes, carbon dots). Combined with a high surface-to-volume ratio and tunable and chemically versatile surfaces, these support broad applications across catalysis, electronics, and life sciences. Moreover, the biocompatible characteristics of NDs enable their controllable interfacial interactions with biological systems, positioning them as excellent candidates for advancing cutting-edge biomedical sciences, particularly through the engineering of efficient material-biointerfaces that facilitate optimal interactions with biological systems. Among various forms of NDs, fluorescent nanodiamonds (FNDs) have emerged as some of the most impactful and rapidly advancing materials, demonstrating strong potential in ultrasensitive spin-enhanced bioimaging, high-precision biosensing, traceable drug delivery, and quantum-enabled biomedical technologies. This Perspective introduces the key principles underlying NDs and FNDs, including their structural properties, synthesis methods, and surface functionalization strategies. It also highlights emerging biomedical applications of NDs and FNDs, with particular emphasis on neurological disorders. Last, the article discusses current challenges in advancing NDs as a multifunctional platform for neural therapies with translational potential toward clinical trials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nuclear and Chemical Sciences Division: Investment Strategy 2023

The mission of the Nuclear and Chemical Sciences (NACS) Division within the Physical and Life Sciences (PLS) Directorate is to advance scientific understanding, capabilities, and technologies in nuclear and particle physics, radiochemistry, forensic science, and isotope systems to support LLNL’s national security mission. NACS Division personnel conduct a diverse range of research activities in particle physics, nuclear physics, radiation detection, nuclear measurements, chemical and nuclear forensic science, nuclear and radiochemistry, isotope geochemistry, and environmental science. These areas are leveraged to address evolving national security challenges. Scientific research provides the foundation for addressing these challenges, and it is also the principal means of attracting, training, and retaining staff scientists who can deliver solutions across the Laboratory’s mission space. The overarching strategy is to position the NACS Division at the nexus between fundamental nuclear and chemical science research and nuclear security applications. This approach will support efforts to recruit, train, and retain top-flight scientists and engineers who will play a key role in executing the Laboratory’s core nuclear security missions, while also enhancing LLNL’s reputation as a center for innovative scientific research. This document describes the strategic vision that will be used to guide key investments aimed at enabling NACS scientists to lead new efforts and meet future challenges.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Insights into pyrrolysine function from structures of a trimethylamine methyltransferase and its corrinoid protein complex

The 22nd genetically encoded amino acid, pyrrolysine, plays a unique role in the key step in the growth of methanogens on mono-, di-, and tri-methylamines by activating the methyl group of these substrates for transfer to a corrinoid cofactor. Previous crystal structures of the Methanosarcina barkeri monomethylamine methyltransferase elucidated the structure of pyrrolysine and provide insight into its role in monomethylamine activation. Herein, we report the second structure of a pyrrolysine-containing protein, the M. barkeri trimethylamine methyltransferase MttB, and its structure bound to sulfite, a substrate analog of trimethylamine. We also report the structure of MttB in complex with its cognate corrinoid protein MttC, which specifically receives the methyl group from the pyrrolysine-activated trimethylamine substrate during methanogenesis. Together these structures provide key insights into the role of pyrrolysine in methyl group transfer from trimethylamine to the corrinoid cofactor in MttC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Sequence-structure-function characterization of the emerging tetracycline destructase family of antibiotic resistance enzymes

Tetracycline destructases (TDases) are flavin monooxygenases which can confer resistance to all generations of tetracycline antibiotics. The recent increase in the number and diversity of reported TDase sequences enables a deep investigation of the TDase sequence-structure-function landscape. Here, we evaluate the sequence determinants of TDase function through two complementary approaches: (1) constructing profile hidden Markov models to predict new TDases, and (2) using multiple sequence alignments to identify conserved positions important to protein function. Using the HMM-based approach we screened 50 high-scoring candidate sequences in Escherichia coli, leading to the discovery of 13 new TDases. The X-ray crystal structures of two new enzymes from Legionella species were determined, and the ability of anhydrotetracycline to inhibit their tetracycline-inactivating activity was confirmed. Using the MSA-based approach we identified 31 amino acid positions 100% conserved across all known TDase sequences. The roles of these positions were analyzed by alanine-scanning mutagenesis in two TDases, to study the impact on cell and in vitro activity, structure, and stability. These results expand the diversity of TDase sequences and provide valuable insights into the roles of important residues in TDases, and flavin monooxygenases more broadly.

60 APPLIED LIFE SCIENCES↗