Publications

Methyl formate (MF; CH₃OCHO) is the smallest representative of esters, which are common components of biodiesel. The present study characterizes the thermal dissociation kinetics of the radicals formed by H atom abstraction from MF—CH₃OCO and CH₂OCHO—through a combination of modeling, experiment, and theory. For the experimental effort, excimer laser photolysis of Cl₂ was used as a source of Cl atoms to initiate reactions with MF in the gas phase. Time-resolved species profiles of MF, Cl₂, HCl, CO₂, CH₃, CH₃Cl, CH₂O, and CH₂ClOCHO were measured and quantified using photoionization mass spectrometry at temperatures of 400–750 K and 10 Torr. The experimental data were simulated using a kinetic model, which was informed by ab initio-based theoretical kinetics calculations and included chlorine chemistry and secondary reactions of radical decomposition products. Here, we calculated the rate coefficients for the H-abstraction reactions Cl + MF → HCl + CH₃OCO (R1a) and Cl + MF → HCl + CH₂OCHO (R1b): k_1a,theory = 6.71 × 10^–15·T^1.14·exp(—606/T) cm³/molecule·s; k_1b,theory = 4.67 × 10^–18·T^2.21·exp(—245/T) cm³/molecule·s over T = 200–2000 K. Electronic structure calculations indicate that the barriers to CH₃OCO and CH₂OCHO dissociation are 13.7 and 31.6 kcal/mol and lead to CH₃ + CO₂ (R3) and CH₂O + HCO (R5), respectively. The master equation-based theoretical rate coefficients are k_3,theory (P = ∞) = 2.94 × 10⁹·T^1.21·exp(—6209/T) s^–1 and k_5,theory (P = ∞) = 8.45 × 10⁸·T^1.39·exp(—15132/T) s^–1 over T = 300–1500 K. The calculated branching fractions into R1a and R1b and the rate coefficient for R5 were validated by modeling of the experimental species time profiles and found to be in excellent agreement with theory. Additionally, we found that the bimolecular reactions CH₂OCHO + Cl, CH₂OCHO + Cl₂, and CH₃ + Cl₂ were critical to accurately model the experimental data and constrain the kinetics of MF-radicals. Inclusion of the kinetic parameters determined in this study showed a significant impact on combustion simulations of larger methyl esters, which are considered as biodiesel surrogates.

An interferometric radioimager provides real-time, high-fidelity radioimaging of high voltage breakdown (HVB) both internal and external to electrical components at sub-nanosecond and sub-millimeter resolution and has an ability to resolve multiple/spatially-extensive HVB simultaneously. Therefore, radioimaging can be used to screen for early life weakness/failure and enable non-destructive screening of defective electrical components. In particular, radioimaging can detect precursors to catastrophic HVB, allowing for early detection of weakness in critical electrical components. Radioimaging can also be used to track HVB and pinpoint defects in electrical components real time, including transformers, capacitors, cables, switches, and microelectronics.

Nearly all metals, alloys, ceramics, and their associated composites are polycrystalline in nature, with grain boundaries that separate well-defined crystalline regions that influence materials properties. In all but the most pure elemental systems, intentional solutes or impurities are present and can segregate to, or less commonly away from, the grain boundaries, in turn influencing boundary behavior, their stability, and associated materials properties. In some cases, grain-boundary segregation can also trigger “phase-like” structural transitions that dramatically alter the essential nature of the boundary. With the development of advanced electron microscopy techniques, researchers can directly observe grain-boundary structures and segregation with atomic precision. Despite such spatial resolution, the underlying mechanisms governing grain-boundary segregation remain difficult to characterize. As a result, computational modeling techniques such as density functional theory, molecular dynamics, mesoscale phase-field, continuum defect theory, and others are important complementary tools to experimental observations for studying grain-boundary segregation behavior. In conclusion, these computational methods offer the ability to explore the underlying formation mechanisms of grain-boundary segregation, elucidate complex segregation behavior, and provide insights into solutions to effectively controlling microstructure.

Conceptually, single-ion polymer electrolytes (SIPE) with the anion bound to the polymer could solve major issues in Li-ion batteries, but their conductivity is too low. Experimentally, weakly interacting anionic groups have the best conductivity. To provide a theoretical basis for this result, density functional theory calculations of the optimized geometries and energies are performed for charged ligands used in SIPE. Comparison is made to neutral ligands found in dual-ion conductors, which demonstrate higher conductivity. Further, the free energy differences between adding and subtracting a ligand are small enough for the neutral ligands to have the conductivity seen experimentally. However, charged ligands have large barriers, implying that lithium transport will coincide with the slow polymer diffusion, as observed in experiments. Overall, SIPE will require additional solvent to achieve a sufficiently high conductivity. Additionally, the binding of mono- and bidentate geometries varies, providing a simple and clear reason that polarizable force fields are required for detailed interactions.

Despite their many advantages, covalent organic frameworks (COFs) built from three-dimensional monomers are synthetically difficult to functionalize. Herein, we provide a new synthetic approach to the functionalization of a three-dimensional covalent organic framework (COF-300) by using a series of solid-state linkage transformations. By reducing the imine linkages of the framework to amine linkages, we produced a more hydrolytically stable material and liberated a nucleophilic amino group, poised for further functionalization. We then treated the amine-linked COF with diverse electrophiles to generate a library of functionalized materials, which we tested for their ability to adsorb perfluoroalkyl substances (PFAS) from water. The framework functionalized with dimethylammonium groups, COF-300-dimethyl, adsorbed more than 250 mg of perfluorooctanoic acid (PFOA) per 1 g of COF, which represents an approximately 14,500-fold improvement over that of COF-300 and underscores the importance of electrostatic interactions to PFAS adsorption performance. In conclusion, this work provides a conceptually new approach to the design and synthesis of functional three-dimensional COFs.

Methyl-ethyl-substituted Criegee intermediate (MECI) is a four-carbon carbonyl oxide that is formed in the ozonolysis of some asymmetric alkenes. MECI is structurally similar to the isoprene-derived methyl vinyl ketone oxide (MVK-oxide) but lacks resonance stabilization, making it a promising candidate to help us unravel the effects of size, structure, and resonance stabilization that influence the reactivity of atmospherically important, highly functionalized Criegee intermediates. We present experimental and theoretical results from the first bimolecular study of MECI in its reaction with SO2, a reaction that shows significant sensitivity to the Criegee intermediate structure. Using multiplexed photoionization mass spectrometry, we obtain a rate coefficient of (1.3 ± 0.3) × 10-10 cm3 s-1 (95% confidence limits, 298 K, 10 Torr) and demonstrate the formation of SO3 under our experimental conditions. Through high-level theory, we explore the effect of Criegee intermediate structure on the minimum energy pathways for their reactions with SO2 and obtain modified Arrhenius fits to our predictions for the reaction of both syn and anti conformers of MECI with SO2 (ksyn = 4.42 × 1011 T-7.80exp(−1401/T) cm3 s-1 and kanti = 1.26 × 1011 T-7.55exp(−1397/T) cm3 s-1). Our experimental and theoretical rate coefficients (which are in reasonable agreement at 298 K) show that the reaction of MECI with SO2 is significantly faster than MVK-oxide + SO2, demonstrating the substantial effect of resonance stabilization on Criegee intermediate reactivity.

Sandia’s contribution to the FY23/Q4 GNG milestone was met: Demonstrate one composite hydride material with experimental data showing reversibility (50 cycles) and volumetric capacity ≥ 350 bar compressed gas.

Fast and reliable estimation of engineered fracture geometries is a key factor in controlling undesirable fractures and enhancing stimulation design. Measuring the surface deformation gradient (tilt) for engineered fractures in shallow depths (<1000 m) has been proven a reliable source of data to infer fracture geometry, thanks to the impressive resolution of tiltmeter units (in the order of nano-radians). However, solving the inverse problem requires reliable and fast forward models. In this study, we present a fast and reliable machine-learned surrogate model to estimate the ground surface tilt induced by pressurised fractures. The proposed surrogate model, based on Conditional Generative Adversarial Networks (cGAN), receives a fracture aperture map in XY and XZ planes as input and predicts the corresponding surface tilts (in X and Y directions). The surrogate model with Wasserstein loss and gradient penalty has been trained using 11,000 samples and tested for a range of input parameters such as depth, dip angles, elastic properties, fluid pressures and fracture shapes. The testing results show excellent performance of the surrogate model compared with the forward finite element model for both single and multiple pressurised fractures, while running hundreds to potentially thousands of times faster.

The objective of this project was to evaluate material- and chemical-based solutions for hydrogen storage in rail applications as an alternative to high-pressure hydrogen gas and liquid hydrogen. Three use cases were assessed: yard switchers, long-haul locomotives, and tenders. Four storage options were considered: metal hydrides, nanoporous sorbents, liquid organic hydrogen carriers, and ammonia, using 700 bar compressed hydrogen as a benchmark. The results suggest that metal hydrides, currently the most mature of these options, have the highest potential. Storage in tenders is the most likely use case to be successful, with long-haul locomotives the least likely due to the required storage capacities and weight and volume constraints. Overall, the results are relevant for high-impact regions, such as the South Coast Air Quality Management District, for which an economical vehicular hydrogen storage system with minimal impact on cargo capacity could accelerate adoption of fuel cell electric locomotives. The results obtained here will contribute to the development of technical storage targets for rail applications that can guide future research. Moreover, the knowledge generated by this project will assist in development of material-based storage for stationary applications such as microgrids and backup power for data centers.

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Infrasound observations have grown increasingly important for the monitoring of earthquakes. While large earthquakes generate infrasound that can be detected thousands of kilometers away, there are few near-field observations of infrasound generated by low-magnitude events. We describe preliminary results of the West Texas Acoustic Experiment, during which infrasound sensors collected continuous data in the Permian Basin for a six-month period spanning January—June 2023. During this time, more than 1000 earthquakes with magnitudes between 1.2 and 4.2 occurred within 50 km of the network. We used spectral analysis, array processing, and manual inspection of waveforms to evaluate arrivals of infrasound signals following 84 events with magnitudes between 2.5 and 4.2. Here, we describe eight such events and the infrasound signals associated with each. We find detections of seismic-to-acoustic infrasound signals associated with seven events. We also find strong evidence of a laterally-propagating, purely acoustic wave generated by an M2.9 earthquake.

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Single particle levitation methods are a powerful subset of aerosol instrumentation that allow a wide range of particle properties and processes to be explored. One of the most common forms of single particle levitation uses electric fields and is generally referred to as an electrodynamic balance (EDB). There are many different kinds of EDB's that have been designed with different applications in mind, and a corresponding array of analytical tools have been developed to characterize particles held in these traps. In this tutorial, we review the design and development of the EDB and discuss a range of analytical methods, including electrostatic analysis, light scattering, spectroscopy, and imaging, that allow for measurements of hygroscopic growth, volatility, surface tension and viscosity, diffusion, and phase and morphology. We go on to review recent advanced analytical methods using mass spectrometry to probe particle composition. This review is intended to provide readers with the basic knowledge to set up an EDB platform, design measurement protocols based on the available analytical tools, and run experiments to probe the fundamental properties of aerosol particles relevant to their role in the atmosphere, impacts on clouds and climate, effects on air quality, role in health and disease, and applications in industrial processes.

Full-field, multi-measurand diagnostics provide rich validation data necessary to improve the product life cycle time of nuclear safety components. Thermophosphor digital image correlation (TP+DIC) is a method of simultaneously measuring strain and temperature fields using patterned phosphor coatings deposited with aerosol deposition (AD). While TP+DIC produces a functional diagnostic, the coating’s reproducibility and the effect of the patterned features on the inferred temperature remains uncharacterized. This NSR&D project provided the opportunity to study two areas: 1) the tunability and repeatability of aerosol deposition and 2) the robustness of aerosol deposition phosphor on deforming substrates. The first area explores the process-property relationship of parameters elucidating the significance of each on the coating. The second area explores the relationship between the features’ characteristics (namely thickness) and the phosphor emission and inferred temperature. Together, the results will lead to the improved accuracy and functionality of TP+DIC for qualification testing of nuclear safety components.

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Mechanical metamaterials are regularly implemented in engineering applications due to their unique properties derived from their structural geometry and material composition. This study incorporates deep reinforcement learning, a subset of machine learning that teaches an agent to complete a task through interactive experiences, into mechanical metamaterial design. The approach creates a design environment for the reinforcement learning agent to iteratively construct metamaterials with tailorable deformation and hysteretic characteristics. Validation involved producing metamaterials with a thermoplastic polyurethane (TPU) base material that exhibited the deformation response of expanded thermoplastic polyurethane (E-TPU) while maximizing or minimizing hysteresis in cyclic compression. This alignment confirmed the feasibility of tailoring deformation and energy manipulation using mechanical metamaterials. The agent's generalizability was tested by tasking it to create various metamaterials with distinct loading deformation responses and specific hysteresis goals in a simulated setting. The agent consistently delivered metamaterials that met loading curve criteria and demonstrated favorable energy return. This work demonstrates the potential of deep reinforcement learning as a rapid and effective tool for designing mechanical metamaterials with customizable traits. It ushers in the possibility of on-demand metamaterial design solutions, opening avenues across industries like footwear, wearables, and medical equipment.

As a follow-up to our previous report on quantum sensing for safeguards, here we delve deeper into quantum-enhanced imaging & spectroscopy and address their relevance to international safeguards. Much of the approaches rely on entangled photons, a quantum phenomenon not possible with classical physics, although just correlated photons will work for some applications, such as ghost imaging. We provide a comprehensive survey of quantum approaches, including multiple entangled photon ghost imaging and spectroscopy techniques. Entangled photons for noise reduction are also described, as well as Non-Line-Of-Sight imaging, compressive techniques, and squeezed light. Of particular interest is the generation of entangled photons with large wavelength separation, such as infrared/visible entangled photon pairs. Such entangled pairs would allow interaction with objects in the IR, such as in the molecular “fingerprint” wavelength region, while the recording device captures the visible photons, thus leveraging the high efficiency and lower cost of visible detectors. Unfortunately, entangled x-ray photons are not practical, which would have been useful for safeguards to interrogate shielded materials. Entangled gamma rays are even further beyond reason. We provide our assessment for application of quantum-enhanced imaging & spectroscopy for international safeguards, including suggested improvements to existing IAEA instruments and destructive assay measurements that are done at IAEA lab facilities.

The U.S. Strategic Petroleum Reserve (SPR) is a crude oil storage system administered by the U.S. Department of Energy. SPR injected a total of over 230 MMB of raw water into 48 caverns as part of oil sales in CY22. Leaching effects were monitored in these caverns to understand how the sales operations may impact the long-term integrity of the caverns. The leaching effects were modeled here using the Sandia Solution Mining Code, SANSMIC. The modeling results indicate that leaching-induced features do not raise concern for the majority of the caverns. In addition to 12 caverns identified in previous leaching reports, seven caverns have been identified for further monitoring based on the results of this report. Twenty-two caverns had pre- and post-leach sonars that were compared with SANSMIC results. Overall, SANSMIC was able to capture the leaching well.

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The Ghareb Formation is a shallowly buried porous chalk in southern Israel that is being considered as a host rock for a geologic nuclear waste repository. Setup and operation of a repository will induce significant mechanical, hydrological and chemical perturbations in the Ghareb. Developing a secure repository requires careful characterization of the rock behavior to different loads. To characterize hydromechanical behavior of the Ghareb, several short- and long-term deformation experiments were conducted. Hydrostatic loading tests were conducted both dry and water-saturated, using different setups to measure elastic properties, time-dependent behavior, and permeability. A set of triaxial tests were conducted to measure the elastic properties and rock strength under differential loading at dry and water-saturated conditions. The hydrostatic tests showed the Ghareb began to deform inelastically around 12–15 MPa, a relatively low effective pressure. Long-term permeability measurements demonstrated that permeability declined with increasing effective pressure and was permanently reduced by ~ 1 order of magnitude after unloading pressure. Triaxial tests showed that water saturation significantly degrades the rock properties of the Ghareb, indicating water-weakening is a significant risk during repository operation. Time-dependent deformation is observed during hold periods of both the hydrostatic and triaxial tests, with deformation being primarily visco-plastic. The rate of deformation and permeability loss is strongly controlled by the effective pressure as well. Additionally, during holds of both hydrostatic and triaxial tests, it is observed that when water-saturated, radial strain surpassed axial strain when above effective pressures of 13–20 MPa. Thus, deformation anisotropy may occur in situ during operations even if the stress conditions are hydrostatic when above this pressure range.

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Neural networks are becoming the cornerstone for national security prediction tasks. However, designing them requires significant research and trial/error, as they have many hyperparameters, including their computation graph (“architecture”). Neural architecture search (NAS) employs secondary optimizers to search for architectures maximizing objectives like accuracy. Evolutionary algorithms (EAs) are the most used class of optimizer for NAS. However, existing Python libraries for writing EAs limit the complexity of experiments a user can design. In this project, we built ARENA, a Python framework that encodes complex, hyper-realistic EAs. ARENA collects detailed information as it runs and is flexible enough to encode non-EA search algorithms. We tested ARENA on 4 toy optimization problems by encoding 3 search algorithms for each—random search, an EA, and simulated annealing. We also designed an EA that performs NAS on the MNIST dataset. Our experiments suggest the potential for immediate mission impact through solving lab-wide optimization problems.

The Daedalus ultrafast x-ray imager is the latest generation in Sandia’s hybrid CMOS detector family. With three frames along an identical line of sight, 1 ns minimum integration time, a higher full well than Icarus, and added features, Daedalus brings exciting new capabilities to diagnostic applications in inertial confinement fusion and high energy density science. In this work, we present measurements of time response, dynamic range, spatial uniformity, pixel cross-talk, and absolute x-ray sensitivity using pulsed optical and x-ray sources. We report a measured 1.5 Me− full well, pixel sensitivity at 9.58 × 10−7 V/e−, and an estimate of spatial uniformity at ∼5% across the sensor array.

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Ultimately, our experiment measures two quantities on an aluminum bar: motion (which modeling must predict) and temperature (which sets thermal boundary conditions). For motion, stereo DIC is a technique to use imaging data to provide displacements relative to a reference image down to 1/100th of a pixel. We use a calibrated infrared imaging method for accurate temperature measurements. We will be capturing simultaneous data and then registering temperature data in space to the same coordinate system as the displacement data. While we will later show that our experiments are repeatable, indicating that separate experiments for motion and temperature would provide similar data, the simultaneous and registered data removes test to test variability as a source of uncertainty for model calibration and reduces the number of time-consuming tests that must be performed.

Uncertainty in severe accident evolution and outcome is driven by event bifurcations that represent distinctive challenges to defensive layers and tend to promote the emergence of discrete classes of core damage and accident risk. This discrete set of "attractor" states arise from the complex networks of competing physical phenomena and conditional event cascades occurring as the overall system degrades – a process that yields increasing degrees of freedom and accident progression pathways. Characterization of these event spaces has proven elusive to more traditional data interrogation methods, but proves tractable by application of more advanced data collection and machine learning approaches. Through application of these approaches we demonstrate a conceptual framework that enables real-time/robust, risk-informed decision-making support to improve accident mitigation and encourage “graceful exits” during low probability, extreme events limiting accident consequences. In this analysis, we simulated over 8,000 short-term station blackout (STSBO) accidents with the state-of-the-art integral severe accident code, MELCOR, and demonstrate the potential for ML approaches to predict simulation outcomes. We chose to pair ML tools with interpretable and mechanistic event trees for the considered STSBO accident space to predict the likelihood of future event paths along the tree. In addition to the current state of the system, we use information from recent trajectories of temperature, pressure, and other physical features, combining both the current state and past trajectories to forecast future event paths. Finally, we simulate the random injection of variable amounts of water to quantify the efficacy of available actions at reducing risks along the many branches in the event tree. We identify scenarios and windows of opportunity to mitigate risk as well as scenarios in which such actions are unlikely to alter the accident end-state.

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and −3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

A near net shape coating is desired to be applied to the outer surface of a capped cylinder (“cake pan”) type substrate using thermal spray technology. A capped cylinder geometry is more complex than simple coupon-level substrate substrates (e.g., flat panels, cylinders) and thus requires a more complex toolpath to deposit a uniform coating. This report documents a practical theoretical approach to calculating relative torch-to-substrate speeds for coating the cylindrical, corner, and cap region of a rotating capped cylinder based on fundamental thermal spray toolpath principles. A preliminary experimental test deposited a thermal spray coating onto a mock substrate using toolpath speeds calculated by the theoretical approach proposed. The mock substrate was metallographically inspected to assess coating uniformity across the cylindrical, corner, and cap region. Inspection of the mock substrate revealed qualitatively uniform coating microstructure and thickness where theoretically predicted, demonstrating the viability of the proposed toolpath method and associated calculations. Pathways forward to optimizing coating uniformity at the cap center are proposed as near term suggested future work.

This paper is concerned with goal-oriented a posteriori error estimation for nonlinear functionals in the context of nonlinear variational problems solved with continuous Galerkin finite element discretizations. A two-level, or discrete, adjoint-based approach for error estimation is considered. The traditional method to derive an error estimate in this context requires linearizing both the nonlinear variational form and the nonlinear functional of interest which introduces linearization errors into the error estimate. In this paper, we investigate these linearization errors. In particular, we develop a novel discrete goal-oriented error estimate that accounts for traditionally neglected nonlinear terms at the expense of greater computational cost. We demonstrate how this error estimate can be used to drive mesh adaptivity. We show that accounting for linearization errors in the error estimate can improve its effectivity for several nonlinear model problems and quantities of interest. We also demonstrate that an adaptive strategy based on the newly proposed estimate can lead to more accurate approximations of the nonlinear functional with fewer degrees of freedom when compared to uniform refinement and traditional adjoint-based approaches.

We present methods to estimate parameters for models for the incidence angle modifier for simulating irradiance on a photovoltaic array. The incidence angle modifier quantifies the fraction of direct irradiance that is reflected away at the array’s face, as a function of the direct irradiance’s angle of incidence. Parameters can be estimated from data and the fitting method can be used to convert between models. We show that the model conversion procedure results in models that produce similar annual insolation on a fixed plane.

Marine aerosol injections are a key component in further understanding of both the potentials of deliberate injection for marine cloud brightening (MCB), a potential climate intervention (CI) strategy, and key aerosol-cloud interaction behaviors that currently form the largest uncertainty in global climate model (GCM) predictions of our climate. Since the rate of spread of aerosols in a marine environment directly translates to the effectiveness and ability of aerosol injections in impacting cloud radiative forcing, it is crucial to understand the spatial and temporal extent of injected-aerosol effects following direct injection into marine environments. The ubiquity of ship-injected aerosol tracks from satellite imagery renders observational validation of new parameterizations possible in 2D, however, 3D compatible data is more scarce, and necessary for the development of subgrid scale parameterizations of aerosol-cloud interactions in GCMs. This report introduces two novel parameterizations of atmospheric aerosol injection behavior suitable for both 3D (GCM-compatible) and 2D (observation-related) modeling. Their applicability is highlighted using a wealth of different observational data: small and larger scale salt-aerosol injection experiments conducted at SNL, 3D large eddy simulations of ship-injected aerosol tracks and 2D satellite images of ship tracks. The power of experimental data in enhancing knowledge of aerosol-cloud interactions is in particular emphasized by studying key aerosol microphysical and optical properties as observed through their mixing in cloud-like environments.

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The research described here was performed as part of the DOE SciDAC project Coupling Approaches for Next Generation Architectures (CANGA). A framework was developed for the derivation of novel algorithms for the multirate time integration of two-component systems coupled across an interface between spatial domains. The multirate aspect means that different time steps are allowed by each component integrator. The framework provides a way to construct multirate integrators with desirable properties related to stability, accuracy and preservation of system invariants. This report describes the framework and summarizes the major results, examples and research products.

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This special memorial issue pays tribute to James (Jim) A. Miller, a giant of combustion science who died in 2021, with a celebration of his enormous influence on the field. We were touched by the responses we received after we sent out the invitations for it. Jim inspired several generations of scientists, who viewed him as a mentor, a father figure, and a friend. Together with Nils Hansen and Peter Glarborg, we have written a detailed account on his life and work. Furthermore, it appeared in this journal shortly after his death; and so here we focus on the scientific areas he had interest in and influence on, and how they relate to the 34 papers in this issue. The topics of these papers span a variety of Jim's interests including nitrogen chemistry, polycyclic aromatic hydrocarbon (PAH) chemistry, oxidation chemistry, energy transfer, prompt dissociations, and codes to facilitate combustion chemistry simulations.

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Mechanical metamaterials are artificial materials with unique global properties due to the structural geometry and material composition of their unit cell. Typically, mechanical metamaterial unit cells are designed such that, when tessellated, they exhibit unique mechanical properties such as zero or negative Poisson's ratio and negative stiffness. Beyond these applications, mechanical metamaterials can be used to achieve tailorable nonlinear deformation responses. Computational methods such as gradient-based topology optimization (TO) and size/shape optimization (SSO) can be implemented to design these metamaterials. However, both methods can lead to suboptimal solutions or a lack of generalizability. Therefore, this research used deep reinforcement learning (DRL), a subset of deep machine learning that teaches an agent to complete tasks through interactive experiences, to design mechanical metamaterials with specific nonlinear deformation responses in compression or tension. The agent learned to design the unit cells by sequentially adding material to a discrete design domain and being rewarded for achieving the desired deformation response. After training, the agent successfully designed unit cells to exhibit desired deformation responses not experienced during training. This work shows the potential of DRL as a high-level design tool for a wide array of engineering applications.

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This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: (1) Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. (2) A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. (3) Device models that are specifically tailored to meet Sandia’s needs, including some radiation-aware devices (for Sandia users only). (4) Object-oriented code design and implementation using modern coding practices. Xyce is a parallel code in the most general sense of the phrase — a message passing parallel implementation — which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows.

American Vacuum Society National Conference presentation on ferroelectric hafnium oxide

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