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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

On the development of the quantitative texture analysis and its application in solving problems of the Earth sciences

A history of texture analysis (TA) evolution is shown, beginning from the first experimental and theoretical attempts to find and characterize preferred orientations of crystal lattices of grains in real polycrystalline samples. Stages of formation of TA theoretical apparatus, its basic elements, and also application of its capabilities for quantitatively describing anisotropic properties of textured samples are discussed. Attention is also paid to the limitations and difficulties associated with the analysis. The application of the quantitative TA apparatus is demonstrated by example describing elastic properties of textured materials up to multiphase samples containing pores and cracks. A wide scope of TA includes the analysis based on neutron scattering which has been effectively developed at the Frank Laboratory of Neutron Physics. A practical opportunity to determine the bulk crystallographic textures of single-phase and multiphase materials is offered by the use of modern neutron diffractometers, including the SKAT diffractometer at the IBR-2 pulsed reactor. This is especially important for studying samples of natural rocks. The examples given show how the neutron scattering data for the quantitative TA are used in combination with other physical and petrological methods for solving fundamental problems of geology and geophysics based on the analysis of a structure and properties of the Earth’s lithosphere matter. The review includes a detailed list of references of original works concerning the TA elaboration, overview publications and monographs, and also information on the most popular TA-related software.

36 MATERIALS SCIENCE↗
DOE OSTI2018

OPERA models for predicting physicochemical properties and environmental fate endpoints

The collection of chemical structure information and associated experimental data for quantitative structure–activity/ property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2–15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission’s Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure–activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency’s CompTox Chemistry Dashboard.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

“MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies

Chemical database searching has become a fxture in many non-targeted identifcation workfows based on highresolution mass spectrometry (HRMS). However, the form of a chemical structure observed in HRMS does not always match the form stored in a database (e.g., the neutral form versus a salt; one component of a mixture rather than the mixture form used in a consumer product). Linking the form of a structure observed via HRMS to its related form(s) within a database will enable the return of all relevant variants of a structure, as well as the related metadata, in a single query. A Konstanz Information Miner (KNIME) workfow has been developed to produce structural representations observed using HRMS (“MS-Ready structures”) and links them to those stored in a database. These MS-Ready structures, and associated mappings to the full chemical representations, are surfaced via the US EPA’s Chemistry Dashboard (https://comptox.epa.gov/dashboard/). This article describes the workfow for the generation and linking of ~700,000 MS-Ready structures (derived from ~760,000 original structures) as well as download, search and export capabilities to serve structure identifcation using HRMS. The importance of this form of structural representation for HRMS is demonstrated with several examples, including integration with the in silico fragmentation software application MetFrag. The structures, search, download and export functionality are all available through the CompTox Chemistry Dashboard, while the MetFrag implementation can be viewed at https://msbi.ipb-halle.de/MetFragBeta/.

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

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

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

Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte

Solid polymer electrolytes have yet to achieve the an ionic conductivity > 1 mS/cm at room temperature for realistic applications. This target implies the need to reduce the effective energy barriers of ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer-Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules, 56, 15, 6051(2023)), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify a series of anions leading to the energy barriers less than 30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers less than 20 kJ/mol, we focused on boron-containing SICs, and performed density functional theory (DFT) based calculations to connect the chemical structures via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion transport. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity $> 1$ mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes. The published datasets include all the temperature-dependent ionic conductivity collected from the literature with literature DOIs, DFT calculated binding energies, and python scripts to analyze data, construct statistical models, and generate plots.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structural models and functional annotations for the Sphagnum divinum proteome

This dataset contains the structural models for the primary transcripts of the Sphagnum divinum proteome. Additionally, for a subset of these proteins, sequence and structural alignment results are provided. This dataset represents the most thorough structural study of a Sphagnum species, also known as peat mosses, by providing three-dimensional atomic resolution structures of the majority of the encoded proteins as well as structural alignment results used in the application of annotating the proteome. References (DOI) AlphaFold v2 Monomer: https://doi.org/10.1038/s41586-021-03819-2. References (DOI) US-align2: https://doi.org/10.1038/s41592-022-01585-1

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural Models and Sequence Alignment Results of the Rhodospirillum rubrum Proteome

This dataset contains the structural models for the primary transcripts of the Rhodospirillum rubrum proteome as well as sequence alignment results for a subset of the encoded proteins. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the R. rubrum proteome to those available in the AlphaFold Protein Structure Database. For proteins that have been annotated as hypothetical, sequence alignment results from the HHblits and SAdLSA alignment methods are provided. These methods are often more capable to resolve sequence homology than other methods. Therefore, the results from both HHblits and SAdLSA are provided to identify possible homologs for these challenging proteins. Numerous sequence databases are utilized for these alignments. References AlphaFold v2 Multimer: https://doi.org/10.1101/2021.10.04.463034. References HHBlits: https://doi.org/10.1186/s12859-019-3019-7. References SAdLSA: https://doi.org/10.3389/fbinf.2021.689960.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural Models and Sequence Alignment Results of the Desulfovibrio vulgaris Proteome

This dataset contains the structural models for the primary transcripts of the Desulfovibrio vulgaris proteome as well as sequence alignment results for a subset of the encoded proteins. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the D. vulgaris proteome to those available in the AlphaFold Protein Structure Database (AFDB). This is a bit more complicated since the proteins reporting in the AFDB originate from an outdated form of the D. vulgaris sequence. The different versions of the D. vulgaris gene annotation are collected in the Chronology subdirectory; further consideration of these changes on the structural space of the proteome are currently underway. For proteins that have been annotated as hypothetical, sequence alignment results from the HHblits and SAdLSA alignment methods are provided. These methods are often more capable to resolve sequence homology than other methods. Therefore, the results from both HHblits and SAdLSA are provided to identify possible homologs for these challenging proteins. Numerous sequence databases are utilized for these alignments. References AlphaFold v2 Multimer: https://doi.org/10.1101/2021.10.04.463034. References HHblits: hhtps://doi.org/10.1186/s12859-019-3019-7. References SAdLSA: hhtps://doi.org/10.3389/fbinf.2021.689960.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural Models of the Rhodopseudomonas palustris Proteome

This dataset contains the structural models for the primary transcripts of the Rhodopseudomonas palustris proteome. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the R. palustris proteome to those available in the AlphaFold Protein Structure Database.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

On the Equivalence of Trapped Colloids, Pinned Vortices, and Spin Ice

We investigate the recently reported analogies between pinned vortices in nano-structured superconductors or colloids in optical traps, and spin ice materials. The frustration of the two models, one describing colloids and vortices, the other describing spin ice, differs essentially. However, their effective energetics is made identical by the contribution of an emergent field associated to a topological charge. This equivalence extends to the local low-energy dynamics of the ice manifold, yet breaks down in lattices of mixed coordination, because of topological charge transfer between sub-latices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Energy Frontier Research Center (EFRC): Center for Materials Science of Nuclear Fuels (CMSNF) Major Accomplishments

The mission of the Center for Material Science of Nuclear Fuels (CMSNF) has been to develop a first-principles-based understanding of thermal transport in the most widely used nuclear fuel, UO 2 , in the presence of defect microstructure associated with radiation environments. The overarching goal within this mission was to develop an experimentally validated multiscale modeling capability directed toward a predictive understanding of the impact of radiation and fission-product induced defects and microstructure on thermal transport in nuclear fuel. Implementation of the mission was accomplished by integrating the physics of thermal transport in crystalline solids with microstructure science under irradiation through multi institutional experimental and computational materials theory teams from Idaho National Laboratory, Oak Ridge National Laboratory, Purdue University, the University of Florida, the University of Wisconsin, and the Colorado School of Mines. The Center’s research focused on five major areas: (i) The fundamental aspects of anharmonicity in UO 2 crystals and its impact on thermal transport; (ii) The effects of radiation microstructure on thermal transport in UO 2 ; (iii) The mechanisms of defect clustering in UO 2 under irradiation; (iv) The effect of temperature and oxygen environment on the stoichiometry of UO 2 ; and (v) The mechanisms of growth of dislocation loops and voids under irradiation. The Center has made important progress in each of these areas, as summarized below.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Silicon Damage Response Function Derivation and Verification: Assessment of Impact on ASTM Standard E722

Unsuccessful attempts by members of the radiation effects community to independently derive the Norgett-Robinson-Torrens (NRT) damage energy factors for silicon in ASTM standard E722-14 led to an investigation of the software coding and data that produced those damage energy factors. The ad hoc collaboration to discover the reason for lack of agreement revealed a coding error and resulted in a report documenting the methodology to produce the response function for the standard. The recommended changes in the NRT damage energy factors for silicon are shown to have significant impact for a narrow energy region of the 1-MeV(Si) equivalent fluence response function. However, when evaluating integral metrics over all neutrons energies in various spectra important to the SNL electronics testing community, the change in the response results in a small decrease in the total 1- MeV(Si) equivalent fluence of ~0.6% compared to the E722-14 response. Response functions based on the newly recommended NRT damage energy factors have been produced and are available for users of both the NuGET and MCNP codes.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Multi-scale modeling of carbon capture systems

The development and scale up of cost effective carbon capture processes is of paramount importance to enable the widespread deployment of these technologies to significantly reduce greenhouse gas emissions. The U.S. Department of Energy initiated the Carbon Capture Simulation Initiative (CCSI) in 2011 with the goal of developing a computational toolset that would enable industry to more effectively identify, design, scale up, operate, and optimize promising concepts. The first half of the presentation will introduce the CCSI Toolset consisting of basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, and high-resolution filtered computationalfluid- dynamics (CFD) submodels. The second half of the presentation will describe a high-fidelity model of a mesoporous silica supported, polyethylenimine (PEI)-impregnated solid sorbent for CO 2 capture. The sorbent model includes a detailed treatment of transport and amine-CO 2 - H 2 O interactions based on quantum chemistry calculations. Using a Bayesian approach for uncertainty quantification, we calibrate the sorbent model to Thermogravimetric (TGA) data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

First Principles Prediction of Structure, Structure Selectivity, and Thermodynamic Stability under Realistic Conditions

Novel materials are often the enabler for new energy technologies. In ab-initio computational materials science, method are developed to predict the behavior of materials starting from the laws of physics, so that properties can be predicted before compounds have to be synthesized and tested. As such, a virtual materials laboratory can be constructed, saving time and money. The objectives of this program were to develop first-principles theory to predict the structure and thermodynamic stability of materials. Since its inception the program focused on the development of the cluster expansion to deal with the increased complexity of complex oxides. This research led to the incorporation of vibrational degrees of freedom in ab-initio thermodynamics, developed methods for multi-component cluster expansions, included the explicit configurational degrees of freedom of localized electrons, developed the formalism for stability in aqueous environments, and culminated in the first ever approach to produce exact ground state predictions of the cluster expansion. Many of these methods have been disseminated to the larger theory community through the Materials Project, pymatgen software, or individual codes. We summarize three of the main accomplishments.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Deposition Velocity Modeling for Turbo FRMAC

The Federal Radiological Monitoring and Assessment Center (FRMAC) has two major assets it uses to perform it responsibilities for responding to a radiological emergency. These are the National Atmospheric Release Advisory Capability (NARAC) and Turbo FRMAC. Recently NARAC updated their deposition model to the state of the art Petroff and Zhang model leading to a significant discrepancy between these two assets in regards to deposition modeling. This report describes the investigation into an appropriate deposition model for Turbo FRMAC to bring the two assets back into line. The ultimate conclusion is that Petroff and Zhang is too complicated for Turbo FRMAC, but the model of Feng is not and is equal to Petroff and Zhang in predictive capability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

IER-539 CED-1: Preliminary Design of a New Horizontal Split Table

This report presents the preliminary design (CED-1) of IER-539, focused on a new Horizontal Split Table (HST) critical assembly machine. The Department of Energy (DOE) National Nuclear Criticality Safety Program (NCSP) funds critical experiment R&D and its Mission and Vision document identifies a long-standing technical gap of a general purpose HST. HSTs have historically been used for experiments that are impractical to assemble on a vertical lift machine (VLM), usually due to their large physical size or weight. While many HSTs were historically employed in the US (including at LLNL), there are currently no surviving HSTs in the US. While there are two critical experiments facilities remaining in the US, the National Critical Experiments Research Center (NCERC) operated by LANL and the Critical Experiments Facility (CX) operated by SNL, these facilities are not equipped with a general purpose HST. There are a number of experiment designs that require an HST, including needs for Defense Programs and Nuclear Energy. Lack of an HST constraints the national ability to perform large and heavy experiments. Large footprints are needed to successfully create mockup experiments for advanced reactors (mock-up cold critical reactor cores, which is a urgent need for new reactor designs using novel materials and longer cycle lengths), to create solution experiments in lattices, or to test nuclear data of heavy metals. A new HST is a vital piece of criticality experiment infrastructure that would allow for the conduct of integral critical experiments to address needs in the nuclear criticality safety, nuclear data, and other DOE stakeholder communities.

42 ENGINEERING↗