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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

Destabilization of the PCNA trimer mediated by its interaction with the NEIL1 DNA glycosylase

The base excision repair (BER) pathway repairs oxidized lesions in the DNA that result from reactive oxygen species generated in cells. If left unrepaired, these damaged DNA bases can disrupt cellular processes such as replication. NEIL1 is one of the 11 human DNA glycosylases that catalyze the first step of the BER pathway, i.e. recognition and excision of DNA lesions. NEIL1 interacts with essential replication proteins such as the ring-shaped homotrimeric proliferating cellular nuclear antigen (PCNA). We isolated a complex formed between NEIL1 and PCNA (±DNA) using size exclusion chromatography (SEC). This interaction was confirmed using native gel electrophoresis and mass spectrometry. Stokes radii measured by SEC hinted that PCNA in complex with NEIL1 (±DNA) was no longer a trimer. Height measurements and images obtained by atomic force microscopy also demonstrated the dissociation of the PCNA homotrimer in the presence of NEIL1 and DNA, while small-angle X-ray scattering analysis confirmed the NEIL1 mediated PCNA trimer dissociation and formation of a 1:1:1 NEIL1-DNA-PCNA(monomer) complex. Furthermore, ab initio shape reconstruction provides insights into the solution structure of this previously unreported complex. Together, these data point to a potential mechanistic switch between replication and BER.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Transfer of training and simulator qualification or myth and folklore in helicopter simulation

Transfer of training studies at Fort Rucker using the backward-transfer paradigm have shown that existing flight simulators are not entirely adequate for meeting training requirements. Using an ab initio training research simulator, a simulation of the UH-1, training effectiveness ratios were developed. The data demonstrate it to be a cost-effective primary trainer. A simulator qualification method was suggested in which a combination of these transfer-of-training paradigms is used to determine overall simulator fidelity and training effectiveness.

Dohme, Jack↗
NASA NTRS2002

Dynamics of a Finite Liquid Oxygen (LOX) Column in a Pulsed Magnetic Field

It is well known that liquid oxygen has a sufficient paramagnetic susceptibility that a strong magnetic field gradient can lift it in the earth's gravitational field. The movement of liquid oxygen is vital to the space program since it one of the primary oxidizers used for propulsion. Transport of liquid oxygen (LOX) via direct interaction of the magnetic fields (B field) with the fluid is a current topic of research and development at Kennedy Space Center, FL. This method of transporting (i.e. pumping) LOX may have particular advantages on Mars and other reduced gravitational environments, namely safety and reliability. This paper will address transport of a magnetic fluid, LOX, via phased-pulsed electromagnets acting on the edge of the column of fluid. The authors have developed a physical model from first-principles for the motion of a magnetic fluid in a particular U-tube geometry subjected to a pulsed magnetic field from an arbitrary solenoidal electromagnet. Experimental data that have been collected from the analogous geometry correlate well to that of the ab-initio calculations.

Youngquist, Robert↗
DOE OSTI2017

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML) methods are emerging as a powerful approach to constructing various forms of transferable atomistic potentials. They have been successfully applied in a variety of applications in chemistry, biology, catalysis, and solid-state physics. However, these models are heavily dependent on the quality and quantity of data used in their fitting. Fitting highly flexible ML potentials, such as neural networks, comes at a cost: a vast amount of reference data is required to properly train these models. We address this need by providing access to a large computational DFT database, which consists of more than 20 M off equilibrium conformations for 57,462 small organic molecules. We believe it will become a new standard benchmark for comparison of current and future methods in the ML potential community.

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

Adsorption and dissociation of molecular hydrogen on orthorhombic β-Mo 2 C and cubic δ-MoC (001) surfaces

Molybdenum carbides are increasingly used in heterogeneously catalyzed hydrogenation reactions, which imply the adsorption and dissociation of molecular hydrogen. In this paper, a systematic density functional theory based study, including or excluding dispersion terms, concerning the interaction and stability of H 2 with cubic δ-MoC(001) and orthorhombic β-Mo 2 C(001) surfaces, is presented. In the latter case the two possible C or Mo terminations are considered. In addition, different situations for the H covered surfaces are examined. Computational results including dispersive forces predict an essentially spontaneous dissociation of H 2 on β-Mo 2 C(001) independently of the surface termination, whereas on δ-MoC(001) molecular hydrogen dissociation implies a small but noticeable energy barrier. Furthermore, the ab initio thermodynamics formalism has been used to compare the stability of different H coverages. In conclusion, core level binding energies and vibrational frequencies are presented with the aim to assist the interpretation of yet unavailable data from X-ray photoelectron and infrared spectroscopies.

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

Active learning for accelerated design of layered materials

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ab initio electroweak corrections to superallowed β decays and their impact on V ud

Radiative corrections are essential for an accurate determination of V ud from superallowed β decays. In view of recent progress in the single-nucleon sector, the uncertainty is dominated by the theoretical description of nucleus-dependent effects, limiting the precision that can currently be achieved for V ud . In this work, we provide a detailed account of the electroweak corrections to superallowed β decays in effective field theory (EFT), including the power counting, potential and ultrasoft contributions, and factorization in the decay rate. Here, we present a first numerical evaluation of the dominant corrections in light nuclei based on quantum Monte Carlo methods, confirming the expectations from the EFT power counting. Finally, we discuss strategies how to extract from data the low-energy constants that parametrize short-distance contributions and whose values are not predicted by the EFT. Combined with advances in ab initio nuclear-structure calculations, this EFT framework allows one to systematically address the dominant uncertainty in V ud , as illustrated in detail for the 14 O → 14 N transition.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

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

Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling

Abstract Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods and play an increasingly important role in the study and design of materials. However, MLIPs are only as accurate and robust as the data on which they are trained. Here, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolates more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with the M3GNet universal potential can be used instead of expensive ab initio MD to rapidly create a large configuration space for target systems. We combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures. This work paves the way for robust high-throughput development of MLIPs across any compositional complexity.

Qi, Ji (ORCID:0000000158089931)↗
DOE OSTI2015

Microstructural evolution of type 304 and 316 stainless steels under neutron irradiation at LWR relevant conditions

Extension of light water reactors' useful life will expose austenitic internal core components to irradiation damage levels beyond 100 displacements per atom (dpa), which will lead to profound microstructural evolution and consequent degradation of macroscopic properties. Microstructural evolution, including Frank loops, cavities, precipitates, and segregation at boundaries and the resultant radiation hardening in type 304 and 316 stainless steel (SS) variants, were studied in this work via experimental characterization and multiple simulation methods. Experimental data for up to 40 heats of type 304SS and 316SS variants irradiated in different reactors to 0.6–120 dpa at 275–375°C were either generated from this work or collected from literature reports. These experimental data were then combined with models of Frank loop and cavity evolution, computational thermodynamics and precipitation, and ab initio and rate theory integrated radiation-induced segregation models to provide insights into microstructural evolution and degradation at higher radiation doses.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Formation of (HCOO – )(H 2 SO 4 ) Anion Clusters: Violation of Gas-Phase Acidity Predictions

Sulfuric acid is commonly known to be a strong acid and, by all counts, should readily donate its proton to formate, which has much higher proton affinity. This conventional wisdom is challenged in this work, where temperature-dependent negative ion photoelectron spectroscopy (NIPES) and theoretical studies demonstrate the existence of (HCOO?)(H2SO4) pair at the energy slightly below the conventional (HCOOH)(HSO4?) structure. Analysis of quantum-mechanical calculations indicates that large proton affinity barrier (~36 kcal/mol), favoring proton transfer to formate, is offset by the gain in inter-molecular interaction energy between HCOO? and H2SO4 through the formation of two strong hydrogen bonds. However, this stabilization comes with severe entropic penalty, requiring the two species in the precise align-ment. As a result, the population of (HCOO?)(H2SO4) drops significantly at higher temperatures, rendering (HCOOH)(HSO4?) to be the dominant species. This phe-nomenon is consistent with the NIPES data, which shows depletion in the spectra assigned to (HCOO?)(H2SO4), and has also been verified by ab initio molecular dynamics simulations.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2014

Electronic Structure, Dielectric Response and Surface Charge Distribution of RGD (1FUV) Peptide

Long and short range molecular interactions govern molecular recognition and self-assembly of biological macromolecules. Microscopic parameters in the theories of these molecular interactions are either phenomenological or need to be calculated within a microscopic theory. We report a unified methodology for the ab initio quantum mechanical (QM) calculation that yields all the microscopic parameters, namely the partial charges as well as the frequency-dependent dielectric response function, that can then be taken as input for macroscopic theories of electrostatic, polar, and van der Waals-London dispersion intermolecular forces. We apply this methodology to obtain the electronic structure of the cyclic tripeptide RGD-4C (1FUV). This ab initio unified methodology yields the relevant parameters entering the long range interactions of biological macromolecules, providing accurate data for the partial charge distribution and the frequency-dependent dielectric response function of this peptide. These microscopic parameters determine the range and strength of the intricate intermolecular interactions between potential docking sites of the RGD-4C ligand and its integrin receptor.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Structural, electronic, vibrational, and dielectric properties of LaBGeO{sub 5} from first principles

Structural, electronic, vibrational, and dielectric properties of LaBGeO{sub 5} with the stillwellite structure are determined based on ab initio density functional theory. The theoretically relaxed structure is found to agree well with the existing experimental data with a deviation of less than 0.2%. Both the density of states and the electronic band structure are calculated, showing five distinct groups of valence bands. Furthermore, the Born effective charge, the dielectric permittivity tensors, and the vibrational frequencies at the center of the Brillouin zone are all obtained. Compared to existing model calculations, the vibrational frequencies are found in much better agreement with the published experimental infrared and Raman data, with absolute and relative rms values of 6.04 cm{sup −1}, and 1.81%, respectively. Consequently, numerical values for both the parallel and perpendicular components of the permittivity tensor are established as 3.55 and 3.71 (10.34 and 12.28), respectively, for the high-(low-)frequency limit.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Local structure of zinc–indium–tin oxide films via grazing-incidence x-ray pair-distribution functions and theoretical methods

A detailed experimental and theoretical study on the local (r ≤ 4.5 Å) atomic structure of amorphous and crystalline zinc–indium–tin oxide (ZITO) thin films using grazing-incidence x-ray Pair-Distribution Functions (PDFs), ab initio Molecular Dynamics (MD), and Empirical Potential Structure Refinement (EPSR) Monte Carlo simulations is presented. High-energy synchrotron x rays, a two-dimensional detector, and different incident angles were used to probe the depth uniformity of five (ZnO) 0.15 (In 2 O 3 ) 0.70 (SnO 2 ) 0.15 films that were deposited via pulsed-laser deposition at growth temperatures (T G ) ranging from 25 to 300 °C. Films deposited at T G ≤ 150 °C were amorphous. The partially crystalline (T G = 200 °C) and fully crystalline (T G = 300 °C) films were highly textured. Both crystalline and amorphous structures were investigated using ab initio MD and EPSR Monte Carlo simulations. The density of the amorphous films determined from the experimental data agreed with MD calculations. Coordination numbers, bond lengths, and distortion for metal–oxygen and for both the edge- and corner-shared In–metal shells up to 4.5 Å obtained from PDF analysis closely agreed with MD and EPSR simulations. There is a pronounced decrease in the edge- and corner-shared In–Zn distances arising from the shorter Zn–O bond length, Zn–O tetrahedral coordination, and In–O–Zn angle in amorphous ZITO compared to its crystalline counterpart. A maximum in electrical mobility was observed for the amorphous film just before crystallization occurred. While the peak is broad, consistent with nearly unchanged overall cation–oxygen coordination in the amorphous films, ESPR results indicate that the tetrahedral coordination follows the conductivity trend.

Grazing Incidence X-ray↗
DOE OSTI2024

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules

Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner.

Mehta, Kshitij↗
NASA NTRS1992

Proton affinity of methyl nitrate - Less than proton affinity of nitric acid

Several state-of-the-art ab initio quantum mechanical methods were used to investigate the equilibrium structure, dipole moments, harmonic vibrational frequencies, and IR intensities of methyl nitrate, methanol, and several structures of protonated methyl nitrate, using the same theoretical methods as in an earlier study (Lee and Rice, 1992) of nitric acid. The ab initio results for methyl nitrate and methanol were found to be in good agreement with available experimental data. The proton affinity (PA) of methyl nitrate was calculated to be 176.9 +/-5 kcal/mol, in excellent agreement with the experimental value 176 kcal/mol obtained by Attina et al. (1987) and less than the PA value of nitric acid. An explanation of the discrepancy of the present results with those of an earlier study on protonated nitric acid is proposed.

Lee, Timothy J.↗
DOE OSTI2015

Ab initio study of thermoelectric properties of doped SnO{sub 2} superlattices

Transparent conductive oxides, such as tin dioxide (SnO{sub 2}), have recently shown to be promising materials for thermoelectric applications. In this work we studied the thermoelectric properties of Fe-, Sb- and Zn-uniformly doping and co-doping SnO{sub 2}, as well as of Sb and Zn planar (or delta)-doped layers in SnO{sub 2} forming oxide superlattices (SLs). Based on the semiclassical Boltzmann transport equations (BTE) in conjunction with ab initio electronic structure calculations, the Seebeck coefficient (S) and figure of merit (ZT) are obtained for these systems, and are compared with available experimental data. The delta doping approach introduces a remarkable modification in the electronic structure of tin dioxide, when compared with the uniform doping, and colossal values for ZT are predicted for the delta-doped oxide SLs. This result is a consequence of the two-dimensional electronic confinement and the strong anisotropy introduced by the doped planes. In comparison with the uniformly doped systems, our predictions reveal a promising use of delta-doped SnO{sub 2} SLs for enhanced S and ZT, which emerge as potential candidates for thermoelectric applications. - Graphical abstract: Band structure and Figure of merit for SnO2:Sb superlattice along Z direction, P. D. Borges, D. E. S. Silva, N. S. Castro, C. R. Ferreira, F. G. Pinto, J. Tronto and L. Scolfaro, Ab initio study of thermoelectric properties of doped SnO2 superlattices. - Highlights: • Thermoelectric properties of SnO{sub 2}-based alloys and superlattices. • High figure of merit is predicted for planar-doped SnO{sub 2} superlattices. • Nanotechnology has an important role for the development of thermoelectric devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗