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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Surface energies of elemental crystals

The surface energy is a fundamental property of the different facets of a crystal that is crucial to the understanding of various phenomena like surface segregation, roughening, catalytic activity, and the crystal's equilibrium shape. Such surface phenomena are especially important at the nanoscale, where the large surface area to volume ratios lead to properties that are significantly different from the bulk. Here, we present the largest database of calculated surface energies for elemental crystals to date. This database contains the surface energies of more than 100 polymorphs of about 70 elements, up to a maximum Miller index of two and three for non-cubic and cubic crystals, respectively. Well-known reconstruction schemes are also accounted for. The database is systematically improvable and has been rigorously validated against previous experimental and computational data where available. We will describe the methodology used in constructing the database, and how it can be accessed for further studies and design of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A new database website for nuclear level densities

We introduce a new open-access, web-based database (http://nld.ascsn.net), Current Archive of Nuclear Density of Levels (CANDL), that hosts experimental nuclear level density (NLD) datasets from a variety of techniques and energy ranges. Built using the Dash framework in Python, the database is designed to be interactive and user-friendly, allowing researchers to search, visualize, fit, and export NLD data with minimal effort. This resource includes data extracted from evaporation spectra, Oslo method variants, and other experimental techniques that cover excitation energies beyond the neutron resonance region. The database supports on-the-fly fitting with two widely-used phenomenological models—the Constant Temperature (CT) model and the Back-Shifted Fermi Gas (BSFG) model—selected for their simplicity and computational efficiency. Future versions aim to include additional datasets and model types, as well as easy-to-use interfaces to data science techniques. Here, this platform offers a vital tool for the nuclear physics, astrophysics, medicine, and reactor design communities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2005

Transferable Calibration Standard Developed for Quantitative Raman Scattering Diagnostics in High-Pressure Flames

Researchers from NASA Glenn Research Center s Combustion Branch and the Ohio Aerospace Institute (OAI) have developed a transferable calibration standard for an optical technique called spontaneous Raman scattering (SRS) in high-pressure flames. SRS is perhaps the only technique that provides spatially and temporally resolved, simultaneous multiscalar measurements in turbulent flames. Such measurements are critical for the validation of numerical models of combustion. This study has been a combined experimental and theoretical effort to develop a spectral calibration database for multiscalar diagnostics using SRS in high-pressure flames. However, in the past such measurements have used a one-of-a-kind experimental setup and a setup-dependent calibration procedure to empirically account for spectral interferences, or crosstalk, among the major species of interest. Such calibration procedures, being non-transferable, are prohibitively expensive to duplicate. A goal of this effort is to provide an SRS calibration database using transferable standards that can be implemented widely by other researchers for both atmospheric-pressure and high-pressure (less than 30 atm) SRS studies. A secondary goal of this effort is to provide quantitative multiscalar diagnostics in high pressure environments to validate computational combustion codes.

Nguyen, Quang-Viet↗
DOE OSTI2021

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO 2 . Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. In conclusion, a strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Accuracy of Thermodynamic Databases for Hydroxyapatite Dissolution Constant

We report that discrepancies have been noted in the solubility constant values of calcium phosphate minerals between various databases employed in widely-used aqueous speciation calculation software programs. This can cause serious errors in the calculated speciation of waters using these software programs. The aim of the present communication was to bring to light these discrepancies. Experimental determinations of the hydroxyapatite solubility product vary by as much as ten orders of magnitude as a result of experimental challenges related to the presence of impurities in the hydroxyapatite used, incongruent dissolution and the contamination of solutions with dissolved carbon dioxide. Finally, it is suggested that the value used in the database Thermo.dat is consistent with experimental data devoid of common experimental problems, while other common databases use values that lead to a vastly overestimated solubility of hydroxyapatite.

58 GEOSCIENCES↗
DOE OSTI2023

An Overview of the Molten Salt Thermal Properties Database–Thermophysical, Version 2.1.1 (MSTDB-TP v.2.1.1)

The current status of the Molten Salt Thermodynamic Database- Thermal Physical (MSTDB-TP) is reported. Building off of MSTDB-TP and through the direction of the Roadmap for thermal property measurement of Molten Salt Reactor systems, MSTDB-TP 2.1.1 has now been released containing a total 448 salt entries (data from 140+independent studies) compared to the original commit of 62, containing thermophysical properties including density, viscosity, thermal conductivity, and heat capacity. Along with the new database release, further advancement of Saline, a C++application programming interface (API), and a graphical user interface (GUI) has facilitated increased user/developer interaction with the database. Furthermore, estimation techniques, first principles calculations (Ab-Initio) and interpolation/extrapolation methods (Redlich-Kister/Muggianu), have shown great promise in the future of thermophysical property determination for filling out compositional spaces and investigating experimentally difficult salts (hazardous/expensive). This report describes the database composition, development and advancement of database tools, and the strategy of advancing and implementing estimation data into future iterations of the database for MSTDB-TP.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

High-throughput assessment of hypothetical zeolite materials for their synthesizeability and industrial deployability

Zeolites are important microporous framework materials, where 200+ structures are known to exist and many millions so-called hypothetical materials can be computationally created. Here, we screen the “Deem” database of hypothetical zeolite structures to find experimentally feasible and industrially relevant materials. We use established and existing criteria and structure descriptors (lattice energy, local interatomic distances, TTT angles), and we develop new criteria which are based on 5-th neighbor distances to T-atoms, tetrahedral order parameters (or, tetrahedrality), and porosity and channel dimensionality. Our filter funnel for screening the most attractive zeolite materials that we construct consists of nine different types of criteria and a total of 53 subcriteria. The funnel reduces the pool of candidate materials from initially >300,000 to 70 and 33, respectively, depending on the channel dimensionality constraint applied (2- and 3-dimensional vs. only 3-dimensional channels). Here, we find that it is critically important to define longer range and more stringent criteria such as the new 5-th neighbor distances to T-atoms and the tetrahedrality descriptor in order to succeed in reducing the huge pool of candidates to a manageable number. Apart from four experimentally achieved structures (BEC, BOG, ISV, SSF), all other candidates are hypothetical frameworks, thus, representing most valuable targets for synthesis and application. Detailed analysis of the screening data allowed us to also propose an exciting future direction how such screening studies as ours could be improved and how framework generating algorithms could be competitively optimized.

36 MATERIALS SCIENCE↗
DOE OSTI2018

ProtDataTherm: A database for thermostability analysis and engineering of proteins

Protein thermostability engineering is a powerful tool to improve resistance of proteins against high temperatures and thereafter broaden their applications. For efficient protein thermostability engineering, different thermostability-classified data sources including sequences and 3D structures are needed for different protein families. However, no data source is available providing such data easily. It is the first release of ProtDataTherm database for analysis and engineering of protein thermostability which contains more than 14 million protein sequences categorized based on their thermal stability and protein family. This database contains data needed for better understanding protein thermostability and stability engineering. Providing categorized protein sequences and structures as psychrophilic, mesophilic and thermophilic makes this database useful for the development of new tools in protein stability prediction. This database is available at http://profiles.bs.ipm.ir/ softwares/protdatatherm. As a proof of concept, the thermostability that improves mutations were suggested for one sample protein belonging to one of protein families with more than 20 mesophilic and thermophilic sequences and with known experimentally measured ΔT of mutations available within ProTherm database.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Natural Products Magnetic Resonance Database (NP-MRD) for 2025

The Natural Products Magnetic Resonance Database or NP-MRD (https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction and retrieval of nuclear magnetic resonance (NMR) data on natural products. The NP-MRD was initially established to support compound de-replication and data dissemination for the natural products community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics and nutrition science fields. Indeed, since its launch in 2021, the NP-MRD has expanded enormously in size, scope and popularity. The current version of NP-MRD now contains nearly 7X more compounds (281,859 vs. 40,908) and 7X more NMR spectra (5.1 million vs. 817,000) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11,000 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from a 165 spectra per year to more than 10,000 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20,000 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, important improvements to the visualization and display of NMR spectra, notable updates to the database’s spectral search utilities and useful additions to support better NMR spectral analysis/prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD’s database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.

Artifical Intelligence↗
DOE OSTI2022

Transfer learning driven design optimization for inertial confinement fusion

Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. Here, in this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

Interval prediction in structural dynamic analysis

Methods for assessing the predictive accuracy of structural dynamic models are examined with attention given to the effects of modal mass, stiffness, and damping uncertainties. The methods are based on a nondeterministic analysis called 'interval prediction' in which interval variables are used to describe parameters and responses that are unknown. Statistical databases for generic modeling uncertainties are derived from experimental data and incorporated analytically to evaluate responses. Covariance matrices of modal mass, stiffness, and damping parameters are propagated numerically in models of large space structures by means of three methods. The test data tend to fall within the predicted intervals of uncertainty determined by the statistical databases. The present findings demonstrate the suitability of using data from previously analyzed and tested space structures for assessing the predictive accuracy of an analytical model.

Hasselman, Timothy K.↗
DOE OSTI2018

FAIR principles and the IEDB: short-term improvements and a long-term vision of OBO-foundry mediated machine-actionable interoperability

The Immune Epitope Database (IEDB), at www.iedb.org, has the mission to make published experimental data relating to the recognition of immune epitopes easily available to the scientific public. By presenting curated data in a searchable database, we have liberated it from the tables and figures of journal articles, making it more accessible and usable by immunologists. Recently, the principles of Findability, Accessibility, Interoperability and Reusability have been formulated as goals that data repositories should meet to enhance the usefulness of their data holdings. We here examine how the IEDB complies with these principles and identify broad areas of success, but also areas for improvement. We describe short-term improvements to the IEDB that are being implemented now, as well as a long-term vision of true ‘machine-actionable interoperability’, which we believe will require community agreement on standardization of knowledge representation that can be built on top of the shared use of ontologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Accelerated Leach Testing of GLASS (ALTGLASS Version 4.0)

The Accelerated Leach Testing of GLASS (ALTGLASS) database is a collection of data from short- and long-term product consistency tests (PCT, ASTM C1285 A and B) on high level waste (HLW) as well as low activity waste (LAW) glasses. The database provides both U.S. and international researchers with an archive of experimental data for the purpose of studying, modeling, or validating existing models of nuclear waste glass corrosion. The ALTGLASS database is maintained and updated by researchers at the Savannah River National Laboratory (SRNL).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Toward Guided Mutagenesis: Gaussian Process Regression Predicts MHC Class II Antigen Mutant Binding

Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptides into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental data set from the Immune Epitope Database, and theoretical data sets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of nine neutral residues from a six-residue subset with an average R 2 coefficient of determination value of 0.62 ± 0.04 (±95% CI), average error of 0.09 ± 0.01 kcal/mol (±95% CI), and with an receiver operating characteristic (ROC) AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R2 value of 0.69 ± 0.04, average error of 0.07 ± 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting seven neutral residues from an eight-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Chemical and Radiation Stability of Ionic Liquids: A Computational Screening Study

Using a variety of density functional theory (DFT) methods, we present a systematic computational screening effort to analyze the chemical and radiation stability for a large number of anions and cations that constitute room-temperature ionic liquids (RTILs). We compute various electronic properties such as the HOMO–LUMO gap, the ionization potential, and the electron affinities for a large library of ions (42 cations and 42 anions). The theoretical analysis provides the most comprehensive characterization of the chemical and radiation stability of individual ions in RTILs to date. Our calculations reveal that cation stability is closely related to constituent alkyl chain length and branching, whereas the anion stability is mostly dictated by ion size and electronegativity. Furthermore, these calculations show that the ωB97XD functional is the most internally consistent for predicting the chemical and radiation stability. In conclusion, these calculations establish a chemical stability database and a theoretical procedure for further experimental and computational studies of RTILs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Physical and optical properties of the International Simple Glass

Abstract Radioactive waste immobilization is a means to limit the release of radionuclides from various waste streams into the environment over a timescale of hundreds to many thousands of years. Incorporation of radionuclide-containing wastes into borosilicate glass during vitrification is one potential route to accomplish such immobilization. To facilitate comparisons and assessments of reproducibility across experiments and laboratories, a six-component borosilicate glass (Si, B, Na, Al, Ca, Zr) known as the International Simple Glass (ISG) was developed by international consensus as a compromise between simplicity and similarity to waste glasses. Focusing on a single glass composition with a multi-pronged approach utilizing state-of-the-art, multi-scale experimental and theoretical tools provides a common database that can be used to assess relative importance of mechanisms and models. Here we present physical property data (both published and previously unpublished) on a single batch of ISG, which was cast into individual ingots that were distributed to the collaborators. Properties from the atomic scale to the macroscale, including composition and elemental impurities, phase purity, density, thermal properties, mechanical properties, optical and vibrational properties, and the results of molecular dynamics simulations are presented. In addition, information on the surface composition and morphology after polishing is included. Although the existing literature on the alteration of ISG is not extensively reviewed here, the results of well-controlled static alteration experiments are presented here as a point of reference for other performance investigations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Computational screening of metal–organic frameworks for biogas purification

Biogas, which involves methane as the combustible compound, is an abundant and renewable energy resource that produces less harmful gases than conventional fossil-based liquid and solid energy sources. Although biogas is of primary interest due to its methane content, it may also include other gases, such as CO 2 , N 2 , H 2 S, and NH 3 . Studies investigating biogas purification often assume that biogas is a binary 50%/50% mixture of methane and carbon dioxide. However, together with N 2 , the toxic contaminants, H 2 S and NH 3 should be accounted for in the biogas mixture for a more realistic separation application. In this study, the computation-ready experimental metal–organic framework (CoRE-MOF) database is screened to identify a shortlist of potentially high-performing MOFs for multi-component biogas purification. Our hierarchical screening process involves (i) removal of MOFs with sub-3 Å pore limiting diameter and uncommon metal nodes, (ii) removal of MOFs that do not selectively capture the four undesired compounds, as determined from grand canonical Monte Carlo (GCMC) simulations for the binary mixtures of CH 4 /(CO 2 or N 2 or H 2 S or NH 3 ), and (iii) ranking of the remaining structures based on various performance metrics determined from GCMC for a five-component mixture. Finally, the relations between some of these metrics and structural properties are discussed to reveal favorable structural characteristics for biogas upgrading.

42 ENGINEERING↗