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DOE OSTI2015

Structure classification and melting temperature prediction in octet AB solids via machine learning

Machine learning methods are being increasingly used in condensed matter physics and materials science to classify crystals structures and predict material properties. However, the reliability of these methods for a given problem, especially when large data sets are unavailable, has not been well studied. By addressing the tasks of classifying crystal structure and predicting melting temperatures of the octet subset of AB solids, we performed such a study and found potential problems with using machine learning methods on relatively small data sets. At the same time, however, we can reaffirm the potential power of such methods for these tasks. In particular, we uncovered an important new material feature, the excess Born effective charge, that significantly increased the accuracy of the predictions for the classification problem we defined. This discovery leads us to propose a new scale for the degree of ionicity and covalency in these solids. More specifically, we partitioned the crystal structures of a set of 75 octet solids into those that are ionic and covalent bonded and thus performed a binary classification task. We found that using the standard indices (r σ ,r π ), suggested by St. John and Bloch several decades ago, enabled an average success in classification of 92%. Using just r σ and the excess Born effective charge ΔZ A of the A atom enabled an average success of 97%, but we also found relatively large variations about these averages that were dependent on how certain machine learning methods were used and for which a standard deviation was not a proper measure of the degree of confidence we can place in either average. Instead, we calculated and report with 95 % confidence that the traditional classification pair predicts an accuracy in the interval [ 89%, 95%] and the accuracy of the new pair lies in the interval [96 %, 99%]. For melting temperature predictions, the size of our data set was 46. We estimate the root-mean-squared error of our resulting model to be 11% of the mean melting temperature of the data, but we note that if the accuracy of this predicted error is itself measured, our estimated fitting error itself has a root-mean-square error of 50%. In short, what we illustrate is that classification and regression predictions can vary significantly, depending on the details of how machine learning methods are applied to small data sets. This variation makes it important, if not essential, to average the predictions and compute confidence intervals about these averages to report results meaningfully. However, when properly used, these statistical methods can advance our understanding and improve predictions of material properties even for small data sets.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Experimental Assessment of Elastic Modulus vs. Relative Density for Stretch- and Bend-Dominated Lattices

This project was designed to study the possibility of using structural properties of lattices to replicate the material properties of certain hard to manufacture designs and use topology optimization to determine the lattice type and density required to mimic these properties. The integration of additively manufactured lattice structures with topology optimization highlights the need for well characterized mechanical properties and uncertainty analyses to insure these optimized structures respond as predicted. Stereolithographically printed octet and rhombic dodecahedron lattices were manufactured at 10%, 25% and 65% density by volume. As a separate task, yet integrated into this work, finite element analysis (FEA) was used to predict the printed lattice’s mechanical properties, which were then compared to our experimental results. After comparing FEA and experimentallymeasured elastic moduli, it was determined that the FEA provides highly reliable predictions for the modulus of these printed lattice structures. These lattices also exhibited greater tensile stiffness than that of the solid material demonstrating the flexibility that lattices provide to designing parts with designer structural properties. The accurate printing and reliable modeling of these lattices will enable topology optimization of complex parts from well-characterized rhombic dodecahedron and octet lattice structures of varying densities.

36 MATERIALS SCIENCE↗

DOE OSTI2012

Effect of Manufacturing-Induced Defects on Reliability of Composite Wind Turbine Blades

In support of DOE's efforts on developing affordable, reliable domestic wind power, this ARRA project brought together a strong, complementary team from academia (University of Massachusetts Lowell), two DOE laboratories (NREL and Sandia), and a major wind turbine blade manufacturer (TPI) to address one of the key issues affecting wind power cost and reliability manufacturing-induced defects in the blades. The complexity of this problem required the assembled team's expertise in materials specifically textile and composite structures finite element modeling, composites manufacturing, mechanical characterization, structural dynamics, nondestructive inspection (NDI) and structural health monitoring (SHM), sensors, and wind turbine blade testing. This final report summarizes the results of this project.

17 WIND ENERGY↗

DOE OSTI2015

Nonlinear Ultrasonic Diagnosis and Prognosis of ASR Damage in Dry Cask Storage

Alkali-silica reaction (ASR) is a deleterious chemical process that may occur in cement-based materials such as mortars and concretes, where the hydroxyl ions in the highly alkaline pore solution attack the siloxane groups in the siliceous minerals in the aggregates. The reaction produces a cross-linked alkali-silica gel. The ASR gel swells in the presence of water. Expansion of the gel results in cracking when the swelling-induced stress exceeds the fracture toughness of the concrete. As the ASR continues, cracks may grow and eventually coalesce, which results in reduced service life and a decrease safety of concrete structures. Since concrete is widely used as a critical structural component in dry cask storage of used nuclear fuels, ASR damage poses a significant threat to the sustainability of long term dry cask storage systems. Therefore, techniques for effectively detecting, managing and mitigating ASR damage are needed. Currently, there are no nondestructive methods to accurately detect ASR damage in existing concrete structures. The only current way of accurately assessing ASR damage is to drill a core from an existing structure, and conduct microscopy on this drilled cylindrical core. Clearly, such a practice is not applicable to dry cask storage systems. To meet these needs, this research is aimed at developing (1) a suite of nonlinear ultrasonic quantitative nondestructive evaluation (QNDE) techniques to characterize ASR damage, and (2) a physics-based model for ASR damage evolution using the QNDE data. Outcomes of this research will provide a nondestructive diagnostic tool to evaluate the extent of the ASR damage, and a prognostic tool to estimate the future reliability and safety of the concrete structures in dry cask storage systems

42 ENGINEERING↗

DOE OSTI2016

Test-Based Calibration of Safety Factors for Capacity Models

In this paper, a simple procedure to calibrate the safety factor of capacity models is presented. The calibration can be carried out based on any available database of experimental tests, even of limited size. The procedure aims to assess the model capability of predicting the test results and to calibrate the safety factor so that the capacity equation meets the target reliability level required by the code or sought by the calibrator. After predicting each test of the database with the capacity equation under consideration, the test-prediction pairs are checked for the property of linearity, and the relative error for the properties of homoscedasticity and normality. Once these properties are fulfilled—which may require a nonlinear transformation of the test values and/or the predictions—the closed-form equation proposed in this paper is employed to compute a target design value. The model safety factor is finally obtained by comparing such target design value with the design value obtained from the code. The paper also proposes two approximate analytical equations to compute the tolerance factor, used to attain any given fractile, as a function of the (even small) number of tests, with any assigned confidence level. A fundamental outcome of the procedure is that it yields an objective indicator of the model accuracy, measured by the standard deviation of its error, which may be regarded as a parameter useful for selecting the most reliable model among different competing ones. In the long run, the application of the proposed procedure will allow achieving a uniform reliability level throughout all capacity models used in codes and guidelines. A further advantage is that the partial safety factors so derived can be straightforwardly updated when more experiments become available. Finally, as an example, the proposed procedure is herein applied to the ACI 318 shear design capacity equation for concrete members unreinforced in shear.

42 ENGINEERING↗

DOE OSTI2015

A spectral scheme for Kohn–Sham density functional theory of clusters

Starting from the observation that one of the most successful methods for solving the Kohn–Sham equations for periodic systems – the plane-wave method – is a spectral method based on eigenfunction expansion, we formulate a spectral method designed towards solving the Kohn–Sham equations for clusters. This allows for efficient calculation of the electronic structure of clusters (and molecules) with high accuracy and systematic convergence properties without the need for any artificial periodicity. The basis functions in this method form a complete orthonormal set and are expressible in terms of spherical harmonics and spherical Bessel functions. Computation of the occupied eigenstates of the discretized Kohn–Sham Hamiltonian is carried out using a combination of preconditioned block eigensolvers and Chebyshev polynomial filter accelerated subspace iterations. Several algorithmic and computational aspects of the method, including computation of the electrostatics terms and parallelization are discussed. We have implemented these methods and algorithms into an efficient and reliable package called ClusterES (Cluster Electronic Structure). A variety of benchmark calculations employing local and non-local pseudopotentials are carried out using our package and the results are compared to the literature. Convergence properties of the basis set are discussed through numerical examples. Computations involving large systems that contain thousands of electrons are demonstrated to highlight the efficacy of our methodology. The use of our method to study clusters with arbitrary point group symmetries is briefly discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

The 2-loop matter power spectrum and the IR-safe integrand

Large scale structure surveys are likely the next leading probe of cosmological information. It is therefore crucial to reliably predict their observables. The Effective Field Theory of Large Scale Structures (EFTofLSS) provides a manifestly convergent perturbation theory for the weakly non-linear regime, where dark matter correlation functions are computed in an expansion of the wavenumber k over the wavenumber associated to the non-linear scale kNL. To push the predictions to higher wavenumbers, it is necessary to compute the 2-loop matter power spectrum. For equal-time correlators, exactly as with standard perturturbation theory, there are IR divergences present in each diagram that cancel completely in the final result. We develop a method by which all 2-loop diagrams are computed as one integral, with an integrand that is manifestly free of any IR divergences. This allows us to compute the 2-loop power spectra in a reliable way that is much less numerically challenging than standard techniques. We apply our method to scaling universes where the linear power spectrum is a single power law of k, and where IR divergences can particularly easily interfere with accurate evaluation of loop corrections if not handled carefully. In conclusion, we show that our results are independent of IR cutoff and, after renormalization, of the UV cutoff, and comment how the method presented here naturally generalizes to higher loops.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

Comparative band alignment of plasma-enhanced atomic layer deposited high-k dielectrics on gallium nitride

Al{sub 2}O{sub 3} films, HfO{sub 2} films, and HfO{sub 2}/Al{sub 2}O{sub 3} stacked structures were deposited on n-type, Ga-face, GaN wafers using plasma-enhanced atomic layer deposition (PEALD). The wafers were first treated with a wet-chemical clean to remove organics and an in-situ combined H{sub 2}/N{sub 2} plasma at 650 Degree-Sign C to remove residual carbon contamination, resulting in a clean, oxygen-terminated surface. This cleaning process produced slightly upward band bending of 0.1 eV. Additional 650 Degree-Sign C annealing after plasma cleaning increased the upward band bending by 0.2 eV. After the initial clean, high-k oxide films were deposited using oxygen PEALD at 140 Degree-Sign C. The valence band and conduction band offsets (VBOs and CBOs) of the Al{sub 2}O{sub 3}/GaN and HfO{sub 2}/GaN structures were deduced from in-situ x-ray and ultraviolet photoemission spectroscopy (XPS and UPS). The valence band offsets were determined to be 1.8 and 1.4 eV, while the deduced conduction band offsets were 1.3 and 1.0 eV, respectively. These values are compared with the theoretical calculations based on the electron affinity model and charge neutrality level model. Moreover, subsequent annealing had little effect on these offsets; however, the GaN band bending did change depending on the annealing and processing. An Al{sub 2}O{sub 3} layer was investigated as an interfacial passivation layer (IPL), which, as results suggest, may lead to improved stability, performance, and reliability of HfO{sub 2}/IPL/GaN structures. The VBOs were {approx}0.1 and 1.3 eV, while the deduced CBOs were 0.6 and 1.1 eV for HfO{sub 2} with respect to Al{sub 2}O{sub 3} and GaN, respectively.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Differential evolution: Global search problem in LEED-IV surface structural analysis

The search process associated with the quantitative theory–experiment comparison in Low Energy Electron Diffraction surface structural analysis can be very time consuming, especially in the case of complex materials with many atoms in the unit cell. Global search algorithms need to be employed to locate the global minimum of the reliability factor in the multi-dimensional structural parameter space. In this study we investigate the use of the Differential Evolution algorithm in Low Energy Electron Diffraction structural analysis. Despite the simplicity of its mechanism the Differential Evolution algorithm presents an impressive performance when applied to ultra-thin films of BaTiO{sub 3}(001) in a theory–theory comparison. A scaling relation of N{sup (1.47} {sup ±} {sup 0.08)} was obtained, where N is the total number of parameters to be optimized. - Highlights: • We investigated the use of the Differential Evolution algorithm (DE) for the LEED search problem. • The DE method was applied to the optimization of the surface structure of the BaTiO{sub 3}(001) ultra-thin films. • A very favorable scaling relation of N{sup 1.47} was obtained, where N is the total number of parameters to be optimized.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning

Understanding the origins of enhanced reactivity of supported, subnanometer in size, metal oxide clusters is challenging due to the scarcity of methods capable to extract atomic-level information from the experimental data. Due to both the sensitivity of X-ray absorption near edge structure (XANES) spectroscopy to the local geometry around metal ions and reliability of theoretical spectroscopy codes for modeling XANES spectra, supervised machine learning approach has become a powerful tool for extracting structural information from the experimental spectra. Here, we present the application of this method to grazing incidence XANES spectra of size-selective Cu oxide clusters on flat support, measured in operando conditions of the methanation reaction. We demonstrate that the convolution neural network can be trained on theoretical spectra and utilized to “invert” experimental XANES data to obtain structural descriptors—the Cu–Cu coordination numbers. As a result, we were able to distinguish between different structural motifs (Cu 2 O-like and CuO-like) of Cu oxide clusters, transforming in reaction conditions, and reliably evaluate average cluster sizes, with important implications for the understanding of structure, composition, and function relationships in catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

PSA 2025 DPRA for Cyber Optimization

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Evaluating defense options should include quantitative evaluation of overall effectiveness to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation and have difficulty with time dependent scenarios. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related integrity is a requirement set by the U.S. Nuclear Regulatory Commission. But companies are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want reliability analysis while optimizing cost, which requires more than safety modeling methods. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with timing and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues such as state-base explosion found in Markov-based tools. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Exploring interface morphology of a deeply buried layer in periodic multilayer

Long-term durability of a thin film device is strongly correlated with the nature of interface structure associated between different constituent layers. Synthetic periodic multilayer structures are primarily employed as artificial X-ray Bragg reflectors in many applications, and their reflection efficiency is predominantly dictated by the nature of the buried interfaces between the different layers. Herein, we demonstrate the applicability of the combined analysis approach of the X-ray reflectivity and grazing incidence X-ray fluorescence measurements for the reliable and precise determination of a buried interface structure inside periodic X-ray multilayer structures. X-ray standing wave field (XSW) generated under Bragg reflection condition is used to probe the different constituent layers of the W- B{sub 4}C multilayer structure at 10 keV and 12 keV incident X-ray energies. Our results show that the XSW assisted fluorescence measurements are markedly sensitive to the location and interface morphology of a buried layer structure inside a periodic multilayer structure. The cross sectional transmission electron microscopy results obtained on the W-B{sub 4}C multilayer structure provide a deeper look on the overall reliability and accuracy of the XSW method. The method described here would also be applicable for nondestructive characterization of a wide range of thin film based semiconductor and optical devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Structural evolution and atomic dynamics in Ni–Nb metallic glasses: A molecular dynamics study

In this work, the composition and temperature dependence of static and dynamic structures in Ni x Nb 1– x ( x = 50–70 at. %) were systematically studied using molecular dynamics with a new-released semi-empirical embedded atom method potential by Mendelev. The calculated pair correlation functions and the structure factor match well with the experimental data, demonstrating the reliability of the potential within relatively wide composition and temperature ranges. The local atomic structures were then characterized by bond angle distributions and Voronoi tessellation methods, demonstrating that the icosahedral $\langle$0,0,12,0$\rangle$ is only a small fraction in the liquid state but increases significantly during cooling and becomes dominant at 300 K. The most abundant clusters are identified as $\langle$0,0,12,0$\rangle$ and distorted icosahedron$\langle$0,2,8,2$\rangle$. The large fraction of these two clusters hints that the relatively good glass forming ability is near the eutectic point. Unlike Cu–Zr alloys, both the self-diffusion coefficient and shear viscosity are insensitive to compositions upon cooling in Ni–Nb alloys. The breakdown of the Stokes-Einstein relation happens at around 1.6 T g ( T g : glass transition temperature). In the amorphous state, the solid and liquid-like atoms can be distinguished based on the Debye-Waller factor $\langle$ u 2 $\rangle$. The insensitivity of the dynamic properties of Ni–Nb alloys to compositions may result from the relatively simple solidification process in the phase diagram, in which only one eutectic point exists in the studied composition range.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Interfacial oxide and other species in trimethylaluminum-pretreated atomic layer deposition-Al 2 O 3 /GaN characterized by sputter-assisted ToF-SIMS

Deposition techniques, like atomic layer deposition (ALD), are used to form high-quality dielectrics for GaN-based metal–oxide–semiconductor (MOS) gate structures due to the lack of a reliable thermal oxide in GaN. Moreover, interfacial GaO x from pre-existing native oxides is thought to adversely impact channel carrier dynamics and induce undesired threshold voltage shifts in GaN-based MOS gate structures. Exposure of the GaN surface to the trimethylaluminum (TMA) precursor prior to standard alumina ALD decreases the native oxide layer on GaN, but the extent of chemical modification has not been well studied in the context of interface composition in a MOS gate structure. Herein, we compare annealed 55 nm Al 2 O 3 dielectric films on GaN grown using either a water-first ALD process or a process including sequential pulses of TMA immediately before the initiation of Al 2 O 3 ALD. Time-of-Flight Secondary Ion Mass Spectrometry measures differences in the interfacial GaO x content between each ALD film. Here, it also detects surface contaminant species like Si, F, S, and C. Furthermore, we report the formation of an AlN species at the Al 2 O 3 /GaN interface, which is more prominent for the film grown using the TMA pre-pulse step. In general, this work demonstrates that the TMA pre-pulse step is an effective strategy for cleaning substrate surfaces prior to ALD.

Atomic layer deposition↗

DOE OSTI2024

Wear of Wave Energy Converters Mooring Lines Belts

Using a belt as a replacement for a rope on a rotary power take-offs (PTOs) system has become more common for wave energy converters, improving cyclic bend over sheave performance with a smaller bending thickness for belts. However, the service life predictions of PTOs are a major concern in design, because belt performance under harsh underwater environments is largely less studied. Here, in this work, the effect of fleet and twist angles on wear life is being investigated both experimentally and numerically. Two three-dimensional equivalent static finite element models are constructed to evaluate the complex stress state of polyurethane-steel belts around steel drums. The first is to capture the response of the experimental investigation performed on the wear life, and the second to predict the wear life of an existing functional PTO. The results show a significant effect for fleet and twist angles on stress concentrations and estimated service life.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Micromechanical Surrogate Machine Learning Model for Creep Deformation Modeling

Process variability during the manufacture of gas turbine engine hot section components can significantly affect the material’s resulting microstructure. In casting, for instance, geometric variation within a component (thin sections versus thick sections, radial location) influences cooling rates and the resulting grain size. The high temperature creep response is known to be sensitive to grain size owing to a diffusional creep mechanism which occurs more readily along grain boundaries. Microstructural variation correspondingly drives mechanical behavior which propagates into component scale performance uncertainty. These factors are essential when planning inspection, maintenance, and repair strategies within a reliability framework. These benefits provide opportunities to increase overall energy efficiency through refined margins. Critically, there is an opportunity to bolster existing data-driven reliability models using physics-driven process-structure-property relations. Here we present recent work establishing a framework for evaluating the probabilistic creep performance of high-temperature materials. A novel microstructure-sensitive crystal plasticity finite element model is established that captures both grain boundary and crystallographic deformation effects. The computationally expensive physics model is calibrated using a statistical approach and this high-fidelity model is subsequently used to train a computationally efficient machine learning surrogate model. The surrogate model is essential for sampling a large ensemble of simulated structure-property pair results. The ensemble data are then mined to extract salient trends to be incorporated into a microstructure-sensitive reliability model. The proposed approach represents a novel way to capture microstructure-sensitive trends from physics-based models within a modern reliability framework.

Fernandez-Zelaia, Patxi [ORNL]↗

DOE OSTI2017

Resistance of concrete to different exposures with chloride-based salts

Formation of complex salts (oxychlorides) has been suspected for causing chemical degradation of concrete in cold regions. In this study, conditioned mode (controlled temperature and relative humidity) environmental scanning electron microscopy was specifically used to minimize changes in the crystal structures of oxychlorides phases and thus reliably capture their unaltered morphology and existence/effects on concrete exposed to different de-icing salts under two different environmental conditions (constant low temperature and wetting/drying (W/D) cycles). Formation of acicular flattened blades of 3- and 5-form magnesium oxychloride (MOX) and tiny fibrous crystals as well as subhedral pseudo-hexagonal calcium oxychloride plates (COX) were found in deteriorated concrete specimens, depending on the type of solution. The reversible formation of hydrous and anhydrous COX during W/D cycles had a significant effect on aggravating the kinetics of damage of concrete in this exposure. The combined salt (MgCl{sub 2} + CaCl{sub 2}), which simulates using a synergistic maintenance and protective strategy for concrete pavements in winter, was the most aggressive solution; thus, this practice should be cautiously reconsidered. The incorporation of 30% fly ash had a pronounced effect on improving the concrete resistance to damage as reflected by sound mechanical properties and longevity. Also, the performance of concrete was much enhanced when an innovative supplementary cementitious material, nanosilica, was incorporated in the cementitious system.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Robust nonequilibrium pathways to microcompartment assembly

Significance The structure of the carboxysome resembles a viral capsid, but, unlike many viruses, the monomers of its shell do not appear to fit together in a uniquely preferred way. As a result, the size and shape of this bacterial microcompartment must be determined by the dynamics of its self-assembly process. Using theory and simulation of a model system, we show that the mechanism of assembling such a structure can proceed through nonequilibrium dynamics that are both reliable and controllable. We identify experimentally tunable parameters that modulate the shape and size of the assembled structure, advancing strategies to repurpose natural microcompartments and to create synthetic mimics.

Rotskoff, Grant M.↗