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DOE OSTI2021

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parametrized from MD simulations of complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four order of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example in the case of large molecules or solids. In this work, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parametrize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation timescales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parametrized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide

Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics to theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous λ transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2024

Generalized representative structures for atomistic systems

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40–72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted.

atomic cluster expansion↗

DOE OSTI2020

Integrated Data Analysis to Improve Te and Zeff Measurement on NSTX-U

The multi-energy soft x-ray (ME-SXR) camera is a new concept for a fusion plasma diagnostic based on a pixelated detector with adjustable photon cutoff thresholds. It is a single, compact, solid-state detector which combines spatial and spectral sensitivity and can be configured remotely at run time, making it ideal for a fusion environment. The detector can be configured for application to a wide range of physics problems including impurity transport, electron temperature profiles, and non-thermal effects. The detector has been installed on the Madison Symmetric Torus (MST) as part of a collaboration between Princeton Plasma Physics Laboratory and the University of Wisconsin–Madison to develop the technology and analysis techniques. A full diagnostic forward model of the ME-SXR diagnostic has been developed to enable analysis of the raw photon count data. This model incorporates the atomic physics underlying the plasma emission, diagnostic geometry, and detector response characteristics. Employing this model, a Bayesian analysis approach is used to extract electron temperature from the raw data. This analysis approach uses all available information, clearly quantifies prior information, and handles uncertainties transparently. The ME-SXR camera is being used to study the 3D (helical, stellarator-like) state that forms in the MST device. This helical core structure has improved energy confinement. The transition to the helical state is accompanied by a rise in electron temperature in the central part of the helical structure, but then this enhancement fluctuates. The characteristics of this fluctuation are being compared to a magneto-hydrodynamics model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. Here, the analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1994

Reaction and Protection of Electrical Wire Insulators in Atomic-oxygen Environments

Atomic-oxygen erosion on spacecraft in low Earth orbit is an issue which is becoming increasingly important because of the growing number of spacecraft that will fly in the orbits which have high concentrations of atomic oxygen. In this investigation, the atomic-oxygen durability of three types of electrical wire insulation (carbon-based, fluoropolymer, and polysiloxane elastomer) were evaluated. These insulation materials were exposed to thermal-energy atomic oxygen, which was obtained by RF excitation of air at a pressure of 11-20 Pa. The effects of atomic-oxygen exposure on insulation materials indicate that all carbon-based materials erode at about the same rate as polyamide Kapton and, therefore, are not atomic-oxygen durable. However, the durability of fluoropolymers needs to be evaluated on a case by case basis because the erosion rates of fluoropolymers vary widely. For example, experimental data suggest the formation of atomic fluorine during atomic-oxygen amorphous-fluorocarbon reactions. Dimethyl polysiloxanes (silicone) do not lose mass during atomic-oxygen exposure, but develop silica surfaces which are under tension and frequently crack as a result of loss of methyl groups. However, if the silicone sample surfaces were properly pretreated to provide a certain roughness, atomic oxygen exposure resulted in a sturdy, non-cracked atomic-oxygen durable SiO2 layer. Since the surface does not crack during such silicone-atomic oxygen reaction, the crack-induced contamination by silicone can be reduced or completely stopped. Therefore, with proper pretreatment, silicone can be either a wire insulation material or a coating on wire insulation materials to provide atomic-oxygen durability.

Hung, Ching-Cheh↗

NASA NTRS2003

High-Level Data Races

Data races are a common problem in concurrent and multi-threaded programming. They are hard to detect without proper tool support. Despite the successful application of these tools, experience shows that the notion of data race is not powerful enough to capture certain types of inconsistencies occurring in practice. In this paper we investigate data races on a higher abstraction layer. This enables us to detect inconsistent uses of shared variables, even if no classical race condition occurs. For example, a data structure representing a coordinate pair may have to be treated atomically. By lifting the meaning of a data race to a higher level, such problems can now be covered. The paper defines the concepts view and view consistency to give a notation for this novel kind of property. It describes what kinds of errors can be detected with this new definition, and where its limitations are. It also gives a formal guideline for using data structures in a multi-threading environment.

Artho, Cyrille↗

DOE OSTI2024

Statistical Validation of Multiple Related Data Sets—Case Study Using Interstellar Boundary Explorer Satellite Data

Abstract Space scientists often face the question of whether data collected by different instruments are measurements of the same source population. This paper proposes a statistical validation method for evaluating the agreement between such related data sets. It offers a detailed case study focused on validating a new data set from the Interstellar Boundary Explorer (IBEX) mission, which serves as a practical how-to guide for similar analyses. Since 2008, the IBEX satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data were in fact susceptible to contamination, having relatively low ENA counts and high background rates. To mitigate this issue, the mission team recently considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to be measuring the same source population as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics provide additional confirmation of source rate coherence across data products. The framework introduced here is general and can be applied to other validation problems both within and outside the field of space physics.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1981

Hot oxygen atoms in the upper atmosphere of Venus

Using data obtained by an ultraviolet spectrometer on the Pioneer-Venus Orbiter, energy and altitude distribution of nonthermal oxygen atoms in the Venus atmosphere for typical daytime and nighttime conditions are calculated using two different numerical methods. Agreement is found between results obtained by the two-stream transport method and the diffusion equation technique. Dissociative recombination of molecular oxygen ions and charge exchange of atomic oxygen ions with the neutral hydrogen and oxygen gas are the source terms, and calculations show that the dissociative recombination term is the dominant one. It is shown that measured hot atom concentrations exceed estimates of the thermal hydrogen atom concentrations derived from Brinton et al. (1980) over most of the daytime exosphere, and the calculated concentrations do so to an even greater extent. It is concluded that hot oxygen atoms are an important component of the dayside exosphere of Venus, their most important effect being the absorption of solar wind.

Nagy, A. F.↗

NASA NTRS1994

The NASA/JPL Evaluation of Oxygen Interactions with Materials-3 (EOIM-3)

The deleterious effects of hyperthermal atomic oxygen (AO) found in low-earth-orbit (LEO) environments on critical flight materials has been known since early shuttle flights. This corrosive effect is of considerable concern because it compromises the performance and longevity of spacecraft/satellite materials deployed for extended periods in LEO. The NASA Evaluation of Oxygen Interactions with Materials-3 (EOIM-3) experiment served as a testbed for a variety of candidate flight materials for space assets. A total of 57 JPL test specimens were present in six subexperiments aboard EOIM-3. In addition to a number of passive exposure materials for flight and advanced technology programs, several subexperiments were included to provide data for understanding the details of atomic oxygen interactions with materials. Data and interpretations are presented for the heated tray, heated strips, solar ultraviolet exposure, and scatterometer subexperiments, along with a detailed description of the exposure conditions experienced by materials in the various experiments. Mass spectra of products emerging from identical samples of a (sup 13)C-enriched polyimide polymer (chemically equivalent to Kapton) under atomic oxygen bombardment in space and in the laboratory were collected. Reaction products unambiguously detected in space were (sup 13)CO, NO, (sup 12)CO2, and (sup 13)CO2. These reaction products and two others, H2O and (sup 12)CO, were detected in the laboratory, along with inelastically scattered atomic and molecular oxygen. Qualitative agreement was seen in the mass spectra taken in space and in the laboratory; the agreement may be improved by reducing the fraction of O2 in the laboratory molecular beam.

Brinza, David E.↗

NASA NTRS1990

On diamond, graphitic and amorphous carbons in primitive extraterrestrial solar system materials

Carbon is among the most abundant elements in the universe and carbon chemistry in meteorites and comets is an important key to understanding many Solar System and interstellar processes. Yet, the mineralogical properties and interrelations between various structural forms of elemental carbon remain ambiguous. Crystalline elemental carbons include rhombohedral graphite, hexagonal graphite, cubic diamond, hexagonal diamond (i.e., lonsdaleite or carbon-2H) and chaoite. Elemental carbon also occurs as amorphous carbon and poorly graphitized (or turbostratic) carbon but of all the forms of elemental carbon only graphite is stable under physical conditions that prevail in small Solar System bodies and in the interstellar medium. The recent discovery of cubic diamond in carbonaceous chondrites and hexagonal diamond in chondritic interplanetary dust particles (IDPs) have created a renewed interest in the crystalline elemental carbons that were not formed by shock processes on a parent body. Another technique, Raman spectroscopy, confirms a widespread occurrence of disordered graphite in the Allende carbonaceous chondrite and in chondritic IDPs. Elemental carbons have also been identified by their characteristic K-edge features in electron energy loss spectra (EELS). However, the spectroscopic data do not necessarily coincide with those obtained by selected area electron diffraction (SAED). In order to interpret these data in terms of rational crystalline structures, it may be useful to consider the principles underlying electron diffraction and spectroscopic analyses. Electron diffraction depends on electron scattering, on the type of atom and the distance between atoms in a crystal lattice. Spectroscopic data are a function of the type of atom and the energy of bonds between atoms. Also, SAED is a bulk sampling technique when compared to techniques such as Raman spectroscopy or EELS. Thus, it appears that combined analyses provide contradictory results and that amorphous, or short-range ordered, carbon identified by conventional TEM imaging and SAED may show evidence for sp(3) bonds in EELS spectra. It is suggested that complex, nanometer-scale, mineralogical interrelations are common to all elemental carbons irrespective of their origin. The subsequent thermal history, or energy balance, will determine the ultimate microstructure.

Rietmeijer, Frans J. M.↗

NASA NTRS1987

Containerless study of metal evaporation by laser induced fluorescence

Laser induced fluorescence (LIF) detection of atomic vapors was used to study evaporation from electromagnetically levitated and CW CO2 laser-heated molybdenum spheres and resistively-heated tungsten filaments. Electromagnetic (EM) levitation in combination with laser heating of tungsten, zirconium, and aluminum specimens was also investigated. LIF intensity vs temperature data were obtained for molybdenum atoms and six electronic states of atomic tungsten, at temperatures up to the melting point of each metal. The detected fraction of the emitted radiation was reduced by self-absorption effects at the higher experimental temperatures. Vaporization enthalpies derived from data for which less than half the LIF intensity was self-absorbed were -636 + or - 24 kJ/g-mol for Mo and 831 + or - 32 kJ/g-mol for W. Space-based applications of EM levitation in combination with radiative heating are discussed.

Schiffman, Robert A.↗

DOE OSTI2020

One Structure, Two Elements—LuGe 2 Superconductor vs Ordinary Metallic Conductor LuSn 2 . A Case Study on How Site-Selective Germanium for Tin Atom Substitution Leads to Modulating of the Charge Distribution

The substitution of chemically similar elements in a given crystal structure is an effective way to enhance physical properties, but the understanding on such improvements is usually impeded because the substitutions are random, and the roles of the different atoms cannot be distinguished by crystallographic symmetry. Herein, we provide a detailed crystallographic analysis and property measurements for the continuous solid solutions LuGe x Sn 2–x (0 < x < 2). The results show that there is no apparent change of the global symmetry, with the end-members LuGe 2 and LuSn 2 , as well as the intermediate LuGe x Sn 2–x compositions adopting the ZrSi 2 type structure (space group Cmcm, Pearson index oC12). Yet, the refinements of the crystal structures from single-crystal X-ray diffraction data show that Ge–Sn atom substitutions are not random, but occur preferentially at the zig-zag chain. The patterned distribution of two group 14 elements leads to a significant variation in chemical bonding and charge ordering within the other structural fragment, the 2D square nets, thereby resulting in tuned electron transport. The enhancement is greater than the typical Bloch-Gruneisen model, and more akin to the parallel-resistor model. Magnetization measurements on single crystals show bulk superconductivity in all LuGe x Sn 2–x samples with shielding fractions as high as 90%. Specific heat data confirm the effect to originate from residual metallic tin in the material, indicating that Sn atom substitutions in the 2D square nets cause disruptions of the hypervalent bonding and local anisotropy, which ultimately leads to vanishing of the superconducting state in the end-member LuGe 2 . This work sheds light on how the complexity in chemical interactions by two different carbon congeners leads to changes in the physical properties and how they can be correlated with the induced charge distribution. Furthermore, these studies also provide a general approach to modulation of charge density, and thus, of emerging physical properties in other classes of intermetallic systems based on the main-group elements of groups 13 to 15.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

IDB Database Tables

The International Database of Reference Gamma-Ray Spectra of Various Nuclear Matter is designed to hold curated gamma spectral data is hosted by the International Atomic Energy Agency on its public facing web site. The database used to hold the spectral data was designed by Sandia National Labs under the auspices of the State Department’s Support Program. This document describes the tables and entity relationships that make up the database.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Photoionization from the Xe 4d orbitals of XeF 2

Here, we present a comparison of the photoionization dynamics of the 4d shell of XeF 2 from threshold to 250 eV to those of the prototypical 4d shell of atomic Xe. The new experimental data include spin–orbit and ligand-field-resolved partial cross sections, photoelectron angular distributions, branching fractions, and lifetime widths for the 4d-hole states. The spin–orbit branching fractions and angular distributions are remarkably similar to the corresponding distributions from atomic Xe across a broad energy interval that includes both the intense shape resonance in the f continuum and a Cooper minimum in the same channel. The angular distributions and branching fractions are also in reasonably good agreement with our first-principles theoretical calculations on XeF 2 . Data are also presented on the lifetime widths of the substate-resolved 4d-hole states of XeF 2 . While the trends in the widths are similar to those in the earlier experimental and theoretical work, the linewidths are considerably smaller than in the previous measurements, which may require some reinterpretation of the decay mechanism. Finally, we present new data and an analysis of the Auger electron spectra for ionization above the 4d thresholds and resonant Auger spectra for several pre-edge features.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Deep Bayesian local crystallography

Abstract The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and surfaces. This plethora of data contains an immense volume of information on materials structures, structural distortions, and physical functionalities. Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information. However, the analysis of atomically resolved images is often based on the adaptation of concepts from macroscopic physics, notably translational and point group symmetries and symmetry lowering phenomena. Here, we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework. We demonstrate the need for a Bayesian definition of symmetry using a simple toy model and demonstrate how this definition can be extended to the experimental data using deep learning networks in a Bayesian setting, namely rotationally invariant variational autoencoders.

36 MATERIALS SCIENCE↗

DOE OSTI2020

RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study

Abstract Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Advances in the electron diffraction characterization of atomic clusters and nanoparticles

Nanoparticles and metallic clusters continue to make a remarkable impact on novel and emerging technologies. In recent years, there have been impressive advances in the controlled synthesis of clusters and their advanced characterization. One of the most common ways to determine the structures of nanoparticles and clusters is by means of X-ray diffraction methods. However, this requires the clusters to crystallize in a similar way to those used in protein studies, which is not possible in many cases. Novel methods based on electron diffraction have been used to efficiently study individual nanoparticles and clusters and these can overcome the obstacles commonly encountered during X-ray diffraction methods without the need for large crystals. These novel methodologies have improved with advances in electron microscopy instrumentation and electron detection. Here, we review advanced methodologies for characterizing metallic nanoparticles and clusters using a variety of electron diffraction procedures. These include selected area electron diffraction, nanobeam diffraction, coherent electron diffraction, precession electron diffraction, scanning transmission electron microcopy diffraction, and high throughput data analytics, which leverage deep learning to reduce the propensity for data errors and translate nanometer and atomic scale measurements into material data.

77 NANOSCIENCE AND NANOTECHNOLOGY↗