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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 1,585 records · Page 88

Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations

Optimal experimental design (OED) is the general formalism of sensor placement and decisions on the data collection strategy for engineered or natural experiments. This problem is prevalent in many critical fields such as battery design, numerical weather prediction, geosciences, environmental and urban studies. State-of-the-art computational methods for experimental design do not accommodate correlation structure in observational errors produced by many expensive-to-operate devices such as X-ray machines, radars, and satellites. Discarding evident data correlations leads to biased results, higher expenses, and waste of valuable resources. We present a general formulation of the OED formalism for model-constrained large-scale Bayesian linear inverse problems, where measurement errors are generally correlated. The proposed approach utilizes the Hadamard product of matrices to formulate the weighted-likelihood, and is valid for both finite as well as infinite-dimensional Bayesian inverse problems. Extensive numerical experiments are carried out for empirical verification of the proposed approach using an advection-diffusion model, where the objective is to optimally place a small set of sensors, under a limited budget, to predict the concentration of a contaminant in a closed and bounded domain.

Correlated observations↗

A.I. for nuclear physics

Nuclear science is concerned with the understanding of the nature of matter, its basic constituents and their interaction to form the elements and the properties we observe. This includes the forms of matter we see around us and also exotic forms such as those that existed in the first moments after the Big Bang and that exist today inside neutron stars. The techniques, tools, and expertise needed for nuclear physics (NP) research are therefore diverse in nature. State-of-the art accelerators are being developed to illuminate the dynamical basis of the core of the atom in terms of the fundamental constituents called quarks and gluons and to increase the number of isotopes with known properties. This scientific infrastructure is reaching scales and complexities that require computational methods for tasks such as anomaly detection in operational data. New methodologies are needed to detect anomalies and to optimize operating parameters, predict failures as well as to discover new optimization algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Conical Intersections at the Nanoscale: Molecular Ideas for Materials

The capacity to predict and describe nonradiative processes in molecules via the identification and characterization of conical intersections is one of the greatest recent successes of theoretical chemistry. Only recently, nonetheless, has this concept been extended to materials science, where nonradiative recombination limits the efficiencies of materials for various optoelectronic applications. Here, we introduce recent advances in the theoretical study of conical intersections in semiconductor nanomaterials. After briefly introducing conical intersections, we argue that specific defects in materials can induce conical intersections between the ground and first excited electronic states, thus introducing pathways for nonradiative recombination. We present recent developments in theoretical methods, computational tools, and chemical intuition for the prediction of such defect-induced conical intersections. Through examples in various nanomaterials, we illustrate the significance of conical intersections for nanoscience. We also discuss challenges facing research in this area and opportunities for progress.

36 MATERIALS SCIENCE↗

Remembering the Work of Phillip L. Geissler: A Coda to His Scientific Trajectory

Phillip L. Geissler made important contributions to the statistical mechanics of biological polymers, heterogeneous materials, and chemical dynamics in aqueous environments. He devised analytical and computational methods that revealed the underlying organization of complex systems at the frontiers of biology, chemistry, and materials science. In this retrospective we celebrate his work at these frontiers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Ab Initio Studies of Discharge Mechanism of MnO 2 in Deep-Cycled Rechargeable Zn/MnO 2 Batteries

Rechargeable alkaline Zn/MnO 2 batteries are an attractive solution for large-scale energy storage applications. Recently, Bi and Cu additives have been used to increase the cycle life and capacity of rechargeable Zn/MnO 2 batteries, with an equivalent of the full two-electron capacity realized for many cycles, in the absence of zinc. However, the mechanism of the effect of Bi and Cu on the performance of rechargeable Zn/MnO 2 batteries has not been investigated in detail. We apply first-principles density functional computational methods to study the discharge mechanisms of the unmodified and Bi/Cu-modified γ -MnO 2 electrodes in rechargeable alkaline Zn/MnO 2 batteries. Using the results of our calculations, we analyze the possible redox reaction pathways in the γ -MnO 2 electrode and identify the electrochemical processes leading to the formation of irreversible discharge reaction products, such as hausmannite and hetaerolite. Our study demonstrates the possibility of formation of intermediate Bi-Mn and Cu-Mn oxides in deep-cycled Bi/Cu-modified MnO 2 electrodes. The formation of intermediate Bi-Mn and Cu-Mn oxides could reduce the rate of accumulation of irreversible reaction products in the MnO 2 electrode and improve the rechargeability and cyclability of Zn/MnO 2 batteries.

25 ENERGY STORAGE↗

OpenMSI Arrayed Analysis Tools (OMAAT) v2.0

Mass spectrometry (MS) is an analytical technique that enables all chemical species within a complex mixture to be resolved and identified based on their mass:charge ratio. MS is an increasingly important tool in life science research and development because it can resolve or separate small molecules, peptides, lipids, proteins, biopolymers and fragments of all of these structures. Unfortunately, the use of MS has been limited for lack of software tools to visualize, manipulate, store and transfer MS data. Ben Bowen, Oliver Ruebel and colleagues made a huge advance on the resolution of these limitations with the invention of "OpenMSI" (#2013-004), a computational method that makes the handling of these very large files (up to 50 Tb) much easier, especially for scientists with limited background in bioinformatics or sophisticated software applications of the type needed to handle such data files. With the OpenMSI Arrayed Analysis Tool (OMAAT, #2016-188) invention, the authors take MS data analysis one step further by providing tolls for automatic sample position optimization and ion selection. Even though sample deposition technologies have improved greatly, there is always the chance of encountering outliers in a predefined roster, especially in very large sample sets. Manual curation of the data would be extremely time consuming, so the inventors have included an algorithm that automatically finds the outliers. OMAAT is written in Python with accompanying Jupyter (formerly iPython) notebook and is fully integrated with OpenMSI.

Bowen, Benjamin↗

Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections

A computational method to automatically detect solar panels on rooftops to aid policy and financial assessment of solar distributed generation. The code automatically classifies parcels containing solar panels in the U.S. as residential or commercial. The code allows the user to specify an input dataset containing parcels and detected solar panels, and then uses information about the parcels and solar panels to automatically classify the rooftops as residential or commercial using machine learning techniques. The zip file containing the code includes sample input and output datasets for the Boston and DC areas.

Morton, AprilM↗

RGM: Random Geological Model Generation Package

This Fortran code is to accompany a manuscript to be submitted to Computers & Geosciences, a high-impact, peer-reviewed journal in computer methods for geosciences research. This Fortran code focuses on generation of synthetic geological models using a multi-randomization strategy. Generating high-fidelity synthetic geological models, including realistic seismic reflector migration images, faults, salt bodies, and relative geological time images, is the key for many supervised machine learning methods that aim to delineate faults and other geological properties of interest from seismic migration images. Our package contains two major functionalities: generating 2D synthetic random geological models and generating 3D synthetic random geological models. In each step of the generation process, we set random values for key properties of a geological model to improve the fidelity of the resulting geological model. The package also includes example codes on how to use the random geological model generation subroutines. We name this package RGM – Random Geological Model generation package.

Gao, Kai↗

Common Electric Power Transmission System Model JSON Schema Specification

The Common Electric Power Transmission System Model (CTM) is an intuitive, extensible, language-agnostic, and error-resistant specification of electric power network components parameter names and units, and relation between components, intended for use by the research community developing new computational methods for power systems operations and simulation. Power system datasets following the CTM specification can be read as dictionaries and manipulated in that form in most programming languages (e.g., Python, Julia, C++). This standard data structure in CTM makes it easy to work in multiple power systems domains (e.g., economic operation, reliability assessment, electricity markets, stability assessment, etc.) without requiring conversions between use-case-specific file formats with information loss in the process. This repository specifies CTM as a JSON Schema, provides documentation, derivate (code-generated) implementations of CTM, and example data and usage of the schema for important use cases.

Aravena Solis, Ignacio↗

AlphaBeta: computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants

Stochastic changes in DNA methylation (i.e., spontaneous epimutations) contribute to methylome diversity in plants. Here, we describe AlphaBeta, a computational method for estimating the precise rate of such stochastic events using pedigree-based DNA methylation data as input. We demonstrate how AlphaBeta can be employed to study transgenerationally heritable epimutations in clonal or sexually derived mutation accumulation lines, as well as somatic epimutations in long-lived perennials. Application of our method to published and new data reveals that spontaneous epimutations accumulate neutrally at the genome-wide scale, originate mainly during somatic development and that they can be used as a molecular clock for age-dating trees.

59 BASIC BIOLOGICAL SCIENCES↗

Advances in metabolic modeling of oleaginous microalgae

Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specifc information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. As a result, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.

59 BASIC BIOLOGICAL SCIENCES↗

Automated evaluation of quaternary structures from protein crystals

A correct assessment of the quaternary structure of proteins is a fundamental prerequisite to understanding their function, physico-chemical properties and mode of interaction with other proteins. Currently about 90% of structures in the Protein Data Bank are crystal structures, in which the correct quaternary structure is embedded in the crystal lattice among a number of crystal contacts. Computational methods are required to 1) classify all protein-protein contacts in crystal lattices as biologically relevant or crystal contacts and 2) provide an assessment of how the biologically relevant interfaces combine into a biological assembly. In our previous work we addressed the first problem with our EPPIC (Evolutionary Protein Protein Interface Classifier) method. Here, we present our solution to the second problem with a new method that combines the interface classification results with symmetry and topology considerations. The new algorithm enumerates all possible valid assemblies within the crystal using a graph representation of the lattice and predicts the most probable biological unit based on the pairwise interface scoring. Our method achieves 85% precision (ranging from 76% to 90% for different oligomeric types) on a new dataset of 1,481 biological assemblies with consensus of PDB annotations. Although almost the same precision is achieved by PISA, currently the most popular quaternary structure assignment method, we show that, due to the fundamentally different approach to the problem, the two methods are complementary and could be combined to improve biological assembly assignments.

59 BASIC BIOLOGICAL SCIENCES↗

Metadynamics simulations reveal mechanisms of Na + and Ca 2+ transport in two open states of the channelrhodopsin chimera, C1C2

Cation conducting channelrhodopsins (ChRs) are a popular tool used in optogenetics to control the activity of excitable cells and tissues using light. ChRs with altered ion selectivity are in high demand for use in different cell types and for other specialized applications. However, a detailed mechanism of ion permeation in ChRs is not fully resolved. Here, we use complementary experimental and computational methods to uncover the mechanisms of cation transport and valence selectivity through the channelrhodopsin chimera, C1C2, in the high- and low-conducting open states. Electrophysiology measurements identified a single-residue substitution within the central gate, N297D, that increased Ca 2+ permeability vs. Na + by nearly two-fold at peak current, but less so at stationary current. We then developed molecular models of dimeric wild-type C1C2 and N297D mutant channels in both open states and calculated the PMF profiles for Na + and Ca 2+ permeation through each protein using well-tempered/multiple-walker metadynamics. Results of these studies agree well with experimental measurements and demonstrate that the pore entrance on the extracellular side differs from original predictions and is actually located in a gap between helices I and II. Cation transport occurs via a relay mechanism where cations are passed between flexible carboxylate sidechains lining the full length of the pore by sidechain swinging, like a monkey swinging on vines. In the mutant channel, residue D297 enhances Ca 2+ permeability by mediating the handoff between the central and cytosolic binding sites via direct coordination and sidechain swinging. We also found that altered cation binding affinities at both the extracellular entrance and central binding sites underly the distinct transport properties of the low-conducting open state. This work significantly advances our understanding of ion selectivity and permeation in cation channelrhodopsins and provides the insights needed for successful development of new ion-selective optogenetic tools.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Modulating ion-binding at macromolecular interfaces during (bio)mineralization: A snapshot review for calcium carbonate and calcium phosphate systems

Abstract Local environments have strict influence over (bio)mineralization in calcifying systems. This snapshot review discusses recent insights into the roles of Ca 2+ -macromolecule interactions on the nucleation of calcium carbonate and calcium phosphate minerals. Experimental findings combined with simulations/modeling are providing breakthrough information and raising important questions for future studies. The emerging picture is that both nucleation and growth are driven by local ordering of ions and water about the macromolecule interface, rather than broader properties or molecular class. Tuning macromolecular properties at the atomic scale thus provides opportunities for highly specific controls on mineralization; however, many limitations and challenges remain. We highlight studies employing in-situ atomic force microscopy (AFM) and transmission electron microscopy (TEM) to observe crystallization processes on or near macromolecular substrates. As the distribution and ability of these techniques increases, fundamental studies integrating experimental and computational methods will be crucial to inform a broad range of applications. Graphical abstract

Knight, Brenna M. (ORCID:0000000244000067)↗

Preliminary study of auto-differentiation algorithm in beam dynamics with stochastic process

Modern particle accelerator optimization requires sophisticated computational methods to address the inherently stochastic nature of beam dynamics. This research develops a framework applying AD to SDEs that specifically addresses beam dynamics challenges in particle accelerators, focusing on accurately modeling and optimizing beam behavior in regimes dominated by stochastic processes. By incorporating key physical phenomena such as synchrotron radiation, wakefield effects, and quantum excitation, the framework aims to provide auto differentiation on the figure of merit of the phase space evolution and beam dynamics. The methodology will enable effective optimization method in a dynamic system with stochastic process.

Accelerator Physics↗

Selectivity in Ligand Binding to Uranyl Compounds: A Synthetic, Structural, Thermodynamic and Computational Study

The uranyl cation (UO 2 2+ ) is the most abundant form of uranium on the planet. It is estimated that 4.5 billion tons of uranium in this form exist in sea water. The ability to bind and extract the uranyl cation from aqueous solution while separating it from other elements would provide a limitless source of nuclear fuel. A large body of research concerns the selective recognition and extraction of uranyl. A stable molecule, the cation has a linear O=U=O geometry. The short U-O bonds (1.78 Å) arise from the combination of uranium 5f/6d and oxygen 2p orbitals. Due to the oxygen moieties being multiply bonded, these sites were not thought to be basic enough for Lewis acidic coordination to be a viable approach to sequestration. We believe that the goal of developing a practical system for uranium separation from seawater will not be attained without new insights into our existing fundamental knowledge of actinide chemistry. We posit that detailed studies of the kinetic and thermodynamic factors that influence interactions between f-elements and ligands with a range of donor atoms is essential to any major advance in this important area. The goal of this research is thus to broaden the coordination chemistry of the uranyl ion by studying new ligand systems via synthetic, structural, thermodynamic and computational methods. We anticipate that this fundamental science will find use beyond actinide separation technologies in areas such as nuclear waste remediation and nuclear materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantitative Prediction of Uranium Speciation and Amidoxime Binding in Seawater from Advanced Simulation Techniques

The goal of this project was to quantitatively predict stability of uranium species and thermochemistry of uranium binding with amidoxime ligands in realistic seawater conditions. The specific aims of the project were: (i) determine the stable species of uranium and its solvation structure in realistic seawater conditions; also speciation and solvation of uranium's major competing species in seawater, vanadium; (ii) accurately predict the free energies (and thereby stability constants) of binding between uranium and amidoxime; (iii) accurately predict the reaction enthalpies and entropies of binding between uranium and amidoxime as well as between vanadium and amidoxime. Main results and conclusions are as follows. First-principles molecular dynamic simulations found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable and that one Ca ion binds to the center UO 2 (CO 3 ) 3 4- anion stronger than the other Ca ion. This finding suggests that using time-resolved EXAFS spectra may confirm the asymmetry in binding of the two Ca ions in the aqueous Ca 2 UO 2 (CO 3 ) 3 complex. To consider the common ions, solvation of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater was simulated by classical molecular dynamics simulation. It was found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable in the model seawater. A Na+ ion was found to be closely associated with the Ca 2 UO 2 (CO 3 ) 3 complex by indirectly interacting with one axial oxygen atom of the UO 2 group bridged by a water molecule. In addition, the Na + ion interacts closely with one Ca 2+ ion than the other. The present simulations revealed the key role of common ions such as Na + in impacting the solvation, structure, and apparent charge of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater. The sequential processes of UO 2 2+ binding with the three CO 3 2- groups and the two Ca 2+ ions in pure water and in 0.1 M NaCl were simulated by classical molecular dynamics with both the non-constrained brute-force approach and umbrella sampling. The simulated free energies show excellent agreement with the experiment. The coordination structures of UO 2 (CO 3 ) 3 4- and CaUO 2 (CO 3 ) 3 2- were found to be significantly affected by the presence of Na + ions, leading to a monodentate binding of a carbonate group to U and a water molecule entering the first coordination shell of U. To shed light on the binding between uranyl and glutardiamidoxime (H 2 B), an important model and state-of-the-art ligand for seawater uranium extraction, a suite of computational methods was employed. From molecular dynamics (MD) simulations, it was found that the binding configurations of B 2- with uranyl favor the twofold distorted η 2 binding between the oximate ends (C=N-O-) and U, while HB - prefers the chelating mode for the oximate end with the neutral end being solvated by water. The free energies of sequential ligand binding to form UO 2 B, [UO 2 B 2 ] 2- , and [UO 2 (HB)B] - were simulated with umbrella sampling and very good agreement with the experimental values was achieved, which corroborates the structural insights into the binding mode. State-of-the-art polymeric sorbents employ both amidoximate and carboxylate groups on the side chains to achieve optimal U uptake and selectivity, so we simulated the binding of a model amidoximate–carboxylate bifunctional ligand with uranyl. Classical MD and free-energy simulations in 0.5 M NaCl showed that the carboxylate group binds first to uranyl, leading to a loose intermediate state, and then, the amidoximate group binds, resulting in a more stable and tight chelate state. Binding of the second bifunctional ligand follows a similar process, and the two ligands prefer a trans configuration around the uranyl group. The simulated free energies indicate that the two bifunctional ligands bind with uranyl 14 kcal/mol stronger than the two ligands with only amidoximate groups, confirming an important synergy between amidoximate and carboxylate groups in binding uranyl.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Adaptive Sample Preparation and Target Fabrication for High-Throughput Materials Science

High-repetition-rate brilliant and coherent light sources are significantly increasing the rate at which scientific experiments can be performed. An investment in transformative technologies to make samples of condensed matter could enable a paradigm shift for that field or open new exciting frontiers in high-energy density science (HEDS). This will especially be true for dynamic materials in extremes research or HEDS, where the repetition rate for placing samples into extreme environments is increasing and the samples or targets are irreversibly destroyed in each experiment. Research teams that are able to implement solutions to all aspects of the experimental process and enable a paradigm of high-throughput science will have the greatest success and impact. There are two different directions for adaptive samples. One is research when the number of experiments can be greatly increased with increased volume, such as in high energy density science. The other is when flexibility and agility can aid Bayesian optimization computational methods and speed up the discovery process, as with materials science. Conducting a similar equation-of-state (EOS) experiment took three years on the OMEGA laser facility, compared to five days of shifts at the LCLS XFEL. New materials with improved properties have been found by guided optimization of experimental science. Adaptive sample preparation and target fabrication can create a tremendous return on scientific investment. High-throughput enabling precision science is also a desirable goal. To address these issues, we sought a diverse set of innovative participants who had ideas of how to achieve this paradigm shift. We invited 42 researchers from a variety of disciplines and fields to attend a workshop in College Station, Texas, May 14–16, 2019. Many of the participants had never been in the same room before, and most found it very exciting to hear about new and different research. The goal was twofold: to define the challenges, i.e., “what would we like to be able to do and why it is difficult to do so,” and to describe the progress, approaches and capabilities, as well as the vision, “what we could do if we had more support.”

36 MATERIALS SCIENCE↗