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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MACCS (MELCOR Accident Consequence Code System) User Guide -- Version 4.0

The MELCOR Accident Consequence Code System (MACCS) is used by Nuclear Regulatory Commission (NRC) and various national and international organizations for probabilistic consequence analysis of nuclear power accidents. This User Guide is intended to assist analysts in understanding the MACCS/WinMACCS model and to provide information regarding the code. This user guide version describes MACCS Version 4.0. Features that have been added to MACCS in subsequent versions are described in separate documentation. This User Guide provides a brief description of the model history, explains how to set up and execute a problem, and informs the user of the definition of various input parameters and any constraints placed on those parameters. This report is part of a series of reports documenting MACCS. Other reports include the MACCS Theory Manual, MACCS Verification Report, Technical Bases for Consequence Analyses Using MACCS, as well as documentation for preprocessor codes including SecPop, MelMACCS, and COMIDA2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Nonstoichiometric Salt Intercalation as a Means to Stabilize Alkali Doping of 2D Materials

Although doping with alkali atoms is a powerful technique for introducing charge carriers into physical systems, the resulting charge-transfer systems are generally not air stable. Here, in this paper, we describe computationally a strategy towards increasing the stability of alkali-doped materials that employs stoichiometrically unbalanced salt crystals with excess cations (which could be deposited during, e.g., in situ gating) to achieve doping levels similar to those attained by pure alkali metal doping. The crystalline interior of the salt crystal acts as a template to stabilize the excess dopant atoms against oxidation and deintercalation, which otherwise would be highly favorable. We characterize this doping method for graphene, NbSe 2 , and Bi 2 Se 3 and its effect on direct-to-indirect band gap transitions, 2D superconductivity, and thermoelectric performance. Salt intercalation should be generally applicable to systems which can accommodate this “ionic crystal” doping (and particularly favorable when geometrical packing constraints favor nonstoichiometry).

2-dimensional systems↗
DOE OSTI2023

Hydrogen irradiation-driven computational surface chemistry of lithium oxide and hydroxide

We have investigated, using molecular dynamics, the surface chemistry of hydrogen incident on the amorphous and crystalline lithium oxide and lithium hydroxide surfaces upon being slowed down by a collision cascade and retained in the amorphous surface of either Li 2 O or LiOH. We looked for the bonding of H to the resident structures in the surface to understand a possible chain of chemical reactions that can lead to surface transformation upon H atom impact. Our findings, using Density-Functional Theory (DFT) trained ReaxFF force field/electronegativity equalization method potentials, stress the importance of inclusion of polarization in the dynamics of a Li–O–H system, which is also illustrated by DFT energy minimization and quantum–classical molecular dynamics using tight binding DFT. The resulting polar-covalent chemistry of the studied systems is complex and very sensitive to the instantaneous positions of all atoms as well as the ratio of concentrations of various resident atoms in the surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Electricity Market Dynamics as Smooth Games: Organizing Gradient-Based Learners and Identifying Instabilities

Wide-spread adoption of machine learning and distributed energy production has the potential to fundamentally change behaviours of energy markets. In practice, along with the benefits, previously unseen instabilities have begun emerging in markets currently embracing these technologies. In this report we study electricity markets as dynamical systems arising in smooth games, where energy consumers are modelled as gradient-based learners who interact on a graph through distributed energy producers. We give simple examples of how instabilities can arise even when the consumers’ local objectives seem reasonable and how these instabilities can be remedied. Drawing insights from these examples, we begin developing a general model of optimization-driven electricity market dynamics and give conditions for ensuring local Nash equilibria of the system are stable fixed points of the dynamics. Our results demonstrate how models of electricity market dynamics might be used for deriving formal guarantees in order to stabilize markets and enact regulatory policies in the face of market shocks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Understanding the Stabilization and Tunability of Divalent Europium 2.2.2B Cryptates

Lanthanides such as europium with more accessible divalent states are useful for studying redox stability afforded by macrocyclic organic ligands. Substituted cryptands, such as 2.2.2B cryptand, that increase the oxidative stability of divalent europium also provide coordination environments that support synthetic alterations of Eu(II) cryptate complexes. Two single crystal structures were obtained containing nine-coordinate Eu(II) 2.2.2B cryptate complexes that differ by a single coordination site, the occupation of which is dictated by changes in reaction conditions. A crystal structure containing a [Eu(2.2.2B)Cl] + complex is obtained from a methanol–THF solvent mixture, while a methanol–acetonitrile solvent mixture affords a [Eu(2.2.2B)(CH 3 OH)] 2+ complex. While both crystals exhibit the typical blue emission observed in most Eu(II) containing compounds as a result of 4f 6 5d 1 to 4f 7 transitions, computational results show that the substitution of a Cl– anion in the place of a methanol molecule causes mixing of the 5d excited states in the Eu(II) 2.2.2B cryptate complex. Additionally, magnetism studies reveal the identity of the capping ligand in the Eu(II) 2.2.2B cryptate complex may also lead to exchange between Eu(II) metal centers facilitated by π-stacking interactions within the structure, slightly altering the anticipated magnetic moment. Finally, the synthetic control present in these systems makes them interesting candidates for studying less stable divalent lanthanides and the effects of precise modifications of the electronic structures of low valent lanthanide elements.

Anions↗
DOE OSTI2022

Gamma Radiation-Induced Degradation of Acetohydroxamic Acid (AHA) in Aqueous Nitrate and Nitric Acid Solutions Evaluated by Multiscale Modelling

Acetohydroxamic acid (AHA) has been proposed for inclusion in advanced, single-cycle, used nuclear fuel reprocessing solvent systems for the reduction and complexation of plutonium and neptunium ions. For this application, a detailed description of the degradation of AHA in dilute aqueous nitric acid is required. To this end, we present a comprehensive, multiscale computer model for the coupled radiolytic and hydrolytic degradation of AHA in aqueous sodium nitrate and nitric acid solutions. Rate coefficients for the reactions of AHA and HA with the oxidizing nitrate radical were measured for the first time using pulsed electron radiolysis and used as inputs in the kinetic model. The computer model results are validated by comparison to experimental data from steady state gamma irradiations, for which the agreement is excellent. Here, the presented model accurately predicts the yields of the major degradation products of AHA: acetic acid, hydroxylamine, nitrous oxide, and molecular hydrogen.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

ABINIT: Overview and focus on selected capabilities

ABINIT is probably the first electronic-structure package to have been released under an open-source license about 20 years ago. It implements density functional theory, density-functional perturbation theory (DFPT), many-body perturbation theory (GW approximation and Bethe–Salpeter equation), and more specific or advanced formalisms, such as dynamical mean-field theory (DMFT) and the “temperature-dependent effective potential” approach for anharmonic effects. Relying on planewaves for the representation of wavefunctions, density, and other space-dependent quantities, with pseudopotentials or projector-augmented waves (PAWs), it is well suited for the study of periodic materials, although nanostructures and molecules can be treated with the supercell technique. The present article starts with a brief description of the project, a summary of the theories upon which ABINIT relies, and a list of the associated capabilities. It then focuses on selected capabilities that might not be present in the majority of electronic structure packages either among planewave codes or, in general, treatment of strongly correlated materials using DMFT; materials under finite electric fields; properties at nuclei (electric field gradient, Mössbauer shifts, and orbital magnetization); positron annihilation; Raman intensities and electro-optic effect; and DFPT calculations of response to strain perturbation (elastic constants and piezoelectricity), spatial dispersion (flexoelectricity), electronic mobility, temperature dependence of the gap, and spin-magnetic-field perturbation. The ABINIT DFPT implementation is very general, including systems with van der Waals interaction or with noncollinear magnetism. Community projects are also described: generation of pseudopotential and PAW datasets, high-throughput calculations (databases of phonon band structure, second-harmonic generation, and GW computations of bandgaps), and the library libpaw. ABINIT has strong links with many other software projects that are briefly mentioned.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Insights into Molecular Magnetism in Metal–Metal Bonded Systems as Revealed by a Spectroscopic and Computational Analysis of Diiron Complexes

A pair of bimetallic compounds featuring Fe–Fe bonds, [Fe( i PrNPPh 2 ) 3 FeR] (R = PMe 3 , ≡N t Bu), have been investigated using High-Frequency Electron Paramagnetic Resonance (HFEPR) as well as field- and temperature-dependent 57 Fe nuclear γ resonance (Mössbauer) spectroscopy. To gain insight into the local site electronic structure, we have concurrently studied a compound containing a single Fe(II) in a geometry analogous to that of one of the dimer sites. Our spectroscopic studies have allowed for the assessment of the electronic structure via the determination of the zero-field splitting and 57 Fe hyperfine parameters for the entire series. We also report on our efforts to correlate structure with physical properties in metal–metal bonded systems using ligand field theory guided by quantum chemical calculations. Through the insight gained in this study, we discuss strategies for the design of single-molecule magnets based on polymetallic compounds linked via direct metal–metal bonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. Here we introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated an d human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub

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

Acceleration of Solvation Free Energy Calculation via Thermodynamic Integration Coupled with Gaussian Process Regression and Improved Gelman–Rubin Convergence Diagnostics

The determination of the solvation free energy of ions and molecules holds profound importance across a spectrum of applications spanning chemistry, biology, energy storage, and the environment. Molecular dynamics simulations are powerful tools for computing this critical parameter. Nevertheless, the accurate and efficient calculation of the solvation free energy becomes a formidable endeavor when dealing with complex systems characterized by potent Coulombic interactions and sluggish ion dynamics and, consequently, slow transition across various metastable states. Here, in the present study, we expose limitations stemming from the conventional calculation of the statistical inefficiency g in the thermodynamic integration method, a factor that can hinder the determination of convergence of the solvation free energy and its associated uncertainty. Instead, we propose a robust scheme based on Gelman–Rubin convergence diagnostics. We leverage this improved estimation of uncertainties to introduce an innovative accelerated thermodynamic integration method based on the Gaussian Process regression. This methodology is applied to the calculation of the solvation free energy of trivalent rare-earth elements immersed in ionic liquids, a scenario in which the aforementioned challenges render standard approaches ineffective. The proposed method proves to be effective in computing solvation free energy in situations where traditional thermodynamic integration methods fall short.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics

In this manuscript, we develop multiple machine learning (ML) models to accelerate a scheme for parameterizing site-based models of exciton dynamics from all-atom configurations of condensed phase sexithiophene systems. This scheme encodes the details of a system’s specific molecular morphology in the correlated distributions of model parameters through the analysis of many single-molecule excited-state electronic-structure calculations. These calculations yield excitation energies for each molecule in the system and the network of pair-wise intermolecular electronic couplings. In this work, we demonstrate that the excitation energies can be accurately predicted using a kernel ridge regression (KRR) model with Coulomb matrix featurization. We present two ML models for predicting intermolecular couplings. The first one utilizes a deep neural network and bi-molecular featurization to predict the coupling directly, which we find to perform poorly. The second one utilizes a KRR model to predict unimolecular transition densities, which can subsequently be analyzed to compute the coupling. We find that the latter approach performs excellently, indicating that an effective, generalizable strategy for predicting simple bimolecular properties is through the indirect application of ML to predict higher-order unimolecular properties. Such an approach necessitates a much smaller feature space and can incorporate the insight of well-established molecular physics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models

Representation of soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. The magnitude of this uncertainty can be reduced by accurate representation of environmental controllers of SOC stocks in ESMs. In this study, we used data of environmental factors, field SOC observations, ESM projections and machine learning approaches to identify dominant environmental controllers of SOC stocks and derive functional relationships between environmental factors and SOC stocks. Our derived functional relationships predicted SOC stocks with similar accuracy as the machine learning approach. We used the derived relationships to benchmark the coupled model intercomparison project phase six ESM representation of SOC stocks. We found divergent environmental control representation in ESMs in comparison to field observations. Representation of SOC in ESMs can be improved by including additional environmental factors and representing their functional relationships with SOC consistent with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

MAPSTER: Automated Geospatial Data Sharing – Version 1.4.0

The US Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) developed MAPSTER which is a geospatial data management tool that aggregates, organizes, and shares data from dispersed sources such as unmanned aerial systems (UAS). Built specifically for use in environments where communications may be limited, MAPSTER utilizes two key technologies to effectively manage data in the field and enable easy data sharing with authorized partners: Observer and Checkpoint. Observer is a lightweight software package on an edge device, such as a laptop, that automatically detects newly processed UAS data and sends to a central server called Checkpoint. Checkpoint is a centralized server at ORNL that receives and manages data from all Observer instances. Even in a very low bandwidth environment, Observer can still send information about the UAS data product almost instantly as it generates its own metadata package on the size of KB (kilobytes). MAPSTER is not only for UAS data but for any geospatial data collected at the austere edge and dispersed sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Tsdat: An Open-Source Data Standardization Framework for Marine Energy and Beyond

Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same domain, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications, which rely on large quantities of data from multiple sources. To address these challenges, the open-source Time-Series Data Pipelines (Tsdat) Python framework was developed by Pacific Northwest National Laboratory, with strategic guidance and direction provided by the National Renewable Energy Laboratory and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the marine energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat framework and the data standards within which it operates. A beta version of Tsdat has been released and is being used by several projects in marine energy, wind energy, and building energy systems.

big data↗
DOE OSTI2024

Role of Intermolecular Interactions in Deep Eutectic Solvents for CO 2 Capture: Vibrational Spectroscopy and Quantum Chemical Studies

Recent research and reviews on CO 2 capture methods, along with advancements in industry, have highlighted high costs and energy-intensive nature as the primary limitations of conventional direct air capture and storage (DACS) methods. In response to these challenges, deep eutectic solvents (DESs) have emerged as promising absorbents due to their scalability, selectivity, and lower environmental impact compared to other absorbents. However, the molecular origins of their enhanced thermal stability and selectivity for DAC applications have not been explored before. Therefore, the current study focuses on a comprehensive investigation into the molecular interactions within an alkaline DES composed of potassium hydroxide (KOH) and ethylene glycol (EG). Combining Fourier transform infrared (FT-IR) and quantum chemical calculations, the study reports structural changes and intermolecular interactions induced in EG upon addition of KOH and its implications on CO 2 capture. Experimental and computational spectroscopic studies confirm the presence of noncovalent interactions (hydrogen bonds) within both EG and the KOH-EG system and point to the aggregation of ions at higher KOH concentrations. Additionally, molecular electrostatic potential (MESP) surface analysis, natural bond orbital (NBO) analysis, quantum theory of atoms-in-molecules (QTAIM) analysis, and reduced density gradient-noncovalent interaction (RDG-NCI) plot analysis elucidate changes in polarizability, charge distribution, hydrogen bond types, noncovalent interactions, and interaction strengths, respectively. Evaluation of explicit and hybrid models assesses their effectiveness in representing intermolecular interactions. This research enhances our understanding of molecular interactions in the KOH-EG system, which are essential for both the absorption and desorption of CO 2 . The study also aids in predicting and selecting DES components, optimizing their ratios with salts, and fine-tuning the properties of similar solvents and salts for enhanced CO 2 capture efficiency.

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

Neural Network Water Model Based on the MB-Pol Many-Body Potential

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. Here, in order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

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

Influence of Pore Surface Structure and Contents on Shock-Induced Collapse and Energy Localization

The majority of computational efforts in the shockwave driven collapse of porosity focus on idealized pore geometries and structures. These give significant insight into the physical mechanisms in play, but omit some potentially important effects of real energetic materials. Here, to address this, we simulate pore collapse in 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) for four types of pore surfaces and fillings: an idealized cylindrical pore, a pore surface coated with polymer, a melted/amorphous pore surface, and a pore filled with gas molecules. We find that these perturbations to the pore structure result in only minor changes to the highly complex collapse mechanisms. However, significant differences in hotspot temperature are observed, with peak temperatures ranging over several hundred Kelvin. The polymer-coated system has the most effect, lowering the hotspot temperature by 300 K, and also limiting the manifestation of intramolecular strain energy that is known to induce mechanochemistry.

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

CO electroreduction on single-atom copper

Electroreduction of carbon dioxide (CO 2 ) or carbon monoxide (CO) toward C 2+ hydrocarbons such as ethylene, ethanol, acetate and propanol represents a promising approach toward carbon-negative electrosynthesis of chemicals. Fundamental understanding of the carbon–carbon (C-C) coupling mechanisms in these electrocatalytic processes is the key to the design and development of electrochemical systems at high energy and carbon conversion efficiencies. Here, we report the investigation of CO electreduction on single-atom copper (Cu) electrocatalysts. Atomically dispersed Cu is coordinated on a carbon nitride substrate to form high-density copper–nitrogen moieties. Chemisorption, electrocatalytic, and computational studies are combined to probe the catalytic mechanisms. Unlike the Langmuir-Hinshelwood mechanism known for copper metal surfaces, the confinement of CO adsorption on the single-copper-atom sites enables an Eley-Rideal type of C-C coupling between adsorbed (*CO) and gaseous [CO(g)] carbon moxide molecules. The isolated Cu sites also selectively stabilize the key reaction intermediates determining the bifurcation of reaction pathways toward different C 2+ products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗